Code for Female contraception and male castration increase lifespan across vertebrates

Supplmentary Information

1 Setting-up

1.1 Loading packages

# packages ####

#ochaRd
# 
# install.packages("devtools")
# install.packages("tidyverse")
# #install.packages("metafor")
# install.packages("patchwork")
# install.packages("R.rsp")
# 
# devtools::install_github("daniel1noble/orchaRd", force = TRUE, build_vignettes = TRUE)
# remotes::install_github("rvlenth/emmeans", dependencies = TRUE, build_opts = "") 
# 
# #emmeans
# remotes::install_github("rvlenth/emmeans", dependencies = TRUE, build_opts = "")
# # metafor
# install.packages("remotes")
# remotes::install_github("wviechtb/metafor")

# loading
pacman::p_load(tidyverse,
               metafor,
               pander,
               stringr,
               ape,
               kableExtra,
               patchwork,
               lme4,
               readxl,
               emmeans,
               rotl,
               orchaRd,
               clubSandwich,
               MuMIn,
               png,
               grid,
               here,
               formatR,
               naniar,
               GoodmanKruskal,
               ggalluvial,
               ggplot2,
               cowplot,
               apextra,
               ggimage,
               ggstance,
               ggtree,
               apextra,
               phytools
)

# need for metafor to understand MuMin 
eval(metafor:::.MuMIn)

1.2 Loading custom functions

# custom functions 

# function for getting lnRR for proportional data (mortality)
# older version
# lnrrp <- function(m1, m2, n1, n2) {
#   # arcsine transforamtion
#   asin_trans <- function(p) { asin(sqrt(p)) }
#   # SD for arcsine distribution (see Wiki - https://en.wikipedia.org/wiki/Arcsine_distribution)
#   var1 <- 1/8
#   var2 <- 1/8
#   # lnRR - with 2nd order correction
#   lnrr <- log(asin_trans(m1)/asin_trans(m2)) + 
#     0.5 * ((var1 / (n1 * asin_trans(m1)^2)) - (var2 / (n2 * asin_trans(m2)^2)))   
#   
#   var <- var1 / (n1 * asin_trans(m1)^2) + var1^2 / (2 * n1^2 * asin_trans(m1)^4)  + 
#     var2 / (n2 * asin_trans(m2)^2) + var2^2 / (2 * n2^2 * asin_trans(m2)^4) 
#   
#   invisible(data.frame(yi = lnrr , vi = var))
# }

lnrrp <- function(m1, m2, n1, n2) {
  # if p1 or p2 = 0, turn into 0.025, if p1 or p2 = 1, turn into 0.975
    m1[m1 == 0] <- 0.025
    m1[m1 == 1] <- 0.975
    m2[m2 == 0] <- 0.025
    m2[m2 == 1] <- 0.975
  # arcsine transforamtion
  asin_trans <- function(p) { asin(sqrt(p)) }
  # SD for arcsine distribution (see Wiki - https://en.wikipedia.org/wiki/Arcsine_distribution)
  var1 <- 1/8
  var2 <- 1/8
  # lnRR - with 2nd order correction
  lnrr <- log(asin_trans(m1)/asin_trans(m2)) + 
    0.5 * ((var1 / (n1 * asin_trans(m1)^2)) - (var2 / (n2 * asin_trans(m2)^2))) 
  
  var <- var1 / (n1 * asin_trans(m1)^2) + var1^2 / (2 * n1^2 * asin_trans(m1)^4)  + 
    var2 / (n2 * asin_trans(m2)^2) + var2^2 / (2 * n2^2 * asin_trans(m2)^4) 
  
  invisible(data.frame(yi = lnrr , vi = var))
}


# function to get to lnRR for longevity data (CV required)
# The method proposed in Nakagawa et al (2022) - missing SD method

lnrrm <- function(m1, m2, n1, n2, cv21, cv22) {
  # lnRR - with 2nd order correction
  lnrr <- log(m1/m2) + 
    0.5 * ((cv21 /n1) - (cv22 / n2))    
  
  var <- (cv21 / n1) + ((cv21^2) / (2 * n1^2))  + 
    (cv22/ n2) + ((cv22^2) / (2 * n2^2) )
  
  invisible(data.frame(yi = lnrr , vi = var))
}

# for folded normal distribution see: https://en.wikipedia.org/wiki/Folded_normal_distribution

# folded mean
folded_mu <-function(mean, variance){
  mu <- mean
  sigma <- sqrt(variance)
  fold_mu <- sigma*sqrt(2/pi)*exp((-mu^2)/(2*sigma^2)) + mu*(1 - 2*pnorm(-mu/sigma))
  fold_mu
} 

# folded variance
folded_v <-function(mean, variance){
  mu <- mean
  sigma <- sqrt(variance)
  fold_mu <- sigma*sqrt(2/pi)*exp((-mu^2)/(2*sigma^2)) + mu*(1 - 2*pnorm(-mu/sigma))
  fold_se <- sqrt(mu^2 + sigma^2 - fold_mu^2)
  # adding se to make bigger mean
  fold_v <-fold_se^2
  fold_v
} 


# log response ratio for plotting (replacing just missing data)
# not using CV for drawing the plot
lnrrm2 <- function(m1, m2, n1, n2, sd1, sd2) {
  # lnRR - with 2nd order correction
  cv21 <- (sd1/m1)^2
  cv22 <- (sd2/m2)^2
  
  lnrr <- log(m1/m2) + 
    0.5 * ((cv21 /n1) - (cv22 / n2))    
  
  var <- (cv21 / n1) + ((cv21^2) / (2 * n1^2))  + 
    (cv22/ n2) + ((cv22^2) / (2 * n2^2) )
  
  invisible(data.frame(yi = lnrr , vi = var))
}


# turning survival % into life expectancy
# note that if m1 = treatment lives 100% (happens twice in the data then we use the height observed in the data-set)

lnrre <- function(m1, m2, n1, n2) {
  
  # expected life span transformation
  
  ea1 <- -1/log(m1)
  ea2 <- -1/log(m2)    
  # var using the delta method
  # V is not var like above - it is not sampling SD but error
  V1 <- ((1-m1)*m1/n1)*(1/(m1*log(m1)^2))^2
  V2 <- ((1-m2)*m2/n2)*(1/(m2*log(m2)^2))^2
  
  lnrr <- log(ea1/ea2)
  
  var <- V1 + V2

  
  invisible(data.frame(yi = lnrr , vi = var))
}


#' Title: Contrast name generator
#'
#' @param name: a vector of character strings
cont_gen <- function(name) {
  combination <- combn(name, 2)
  name_dat <- t(combination)
  names <- paste(name_dat[, 1], name_dat[, 2], sep = "-")
  return(names)
}

#' @title get_pred1: intercept-less model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_pred1 <- function (model, mod = " ") {
  name <- firstup(as.character(stringr::str_replace(row.names(model$beta), mod, "")))
  len <- length(name)
  
   if (len != 1) {
        newdata <- diag(len)
        pred <- metafor::predict.rma(model, 
                                     newmods = newdata,
                                     tau2.levels = 1:len)
    }
    else {
        pred <- metafor::predict.rma(model)
  }
  estimate <- pred$pred
  lowerCL <- pred$ci.lb
  upperCL <- pred$ci.ub 
  lowerPR <- pred$cr.lb
  upperPR <- pred$cr.ub 
  
  table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
                  lowerCL = lowerCL, upperCL = upperCL,
                  pval = model$pval,
                  lowerPR = lowerPR, upperPR = upperPR)
}

#' @title get_pred2: normal model
#' @description Function to get CIs (confidence intervals) and PIs (prediction intervals) from rma objects (metafor)
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_pred2 <- function (model, mod = " ") {
  name <- as.factor(str_replace(row.names(model$beta), 
                                paste0("relevel", "\\(", mod,", ref = name","\\)"),""))
  len <- length(name)
  
  if(len != 1){
  newdata <- diag(len)
  pred <- predict.rma(model, intercept = FALSE, newmods = newdata[ ,-1])
  }
  else {
    pred <- predict.rma(model)
  }
  estimate <- pred$pred
  lowerCL <- pred$ci.lb
  upperCL <- pred$ci.ub 
  lowerPR <- pred$cr.lb
  upperPR <- pred$cr.ub 
  
  table <- tibble(name = factor(name, levels = name, labels = name), estimate = estimate,
                  lowerCL = lowerCL, upperCL = upperCL,
                  pval = model$pval,
                  lowerPR = lowerPR, upperPR = upperPR)
}

#' @title mr_results
#' @description Function to put results of meta-regression and its contrasts
#' @param res1: data frame 1
#' @param res1: data frame 2
mr_results <- function(res1, res2) {
  restuls <-tibble(
    `Fixed effect` = c(as.character(res1$name), cont_gen(res1$name)),
    Estimate = c(res1$estimate, res2$estimate),
    `Lower CI [0.025]` = c(res1$lowerCL, res2$lowerCL),
    `Upper CI  [0.975]` = c(res1$upperCL, res2$upperCL),
    `P value` = c(res1$pval, res2$pval),
    `Lower PI [0.025]` = c(res1$lowerPR, res2$lowerPR),
    `Upper PI  [0.975]` = c(res1$upperPR, res2$upperPR),
  )
}


#' @title all_models
#' @description Function to take all possible models and get their results
#' @param model: intercept-less model
#' @param mod: the name of a moderator 

all_models <- function(model, mod = " ", type = "homo") {
  
  # getting the level names out
  level_names <- levels(factor(model$data[[mod]]))
  dat2 <- model$data
  mod <- mod


  run_rma1 <- function(name) {
      VCV1 <- vcalc(vi = dat2$vi,
             cluster = dat2$species,
             obs = dat2$obs_id,
             rho = 0.5)
      
    rma.mv(yi, V = VCV1,
                   mods = ~relevel(dat2[[mod]], ref = name),
                     random = list(
                       ~1|species,
                       ~1|phylogeny,
                       ~1|obs_id),
                     R = list(phylogeny = cor_tree),
                     data = dat2,
                     control = list(optimizer = "Nelder-Mead"))
   }

    run_rma2 <- function(name) {
    
            VCVa <- vcalc(vi = dat2$abs_vi, 
                          species,
                          obs = obs_id,
                    rho = 0.5, data = dat2)
               
               rma.mv(abs_yi, V = VCVa,
               mods = ~relevel(dat2[[mod]], ref = name),
                     random = list(
                       ~1|species,
                       ~1|phylogeny,
                       ~1|obs_id),
                     R = list(phylogeny = cor_tree),
                     data = dat2,
                     control = list(optimizer = "Nelder-Mead"))
   }

# results of meta-regression including all contrast results; taking the last level out ([-length(level_names)])
# this does not work for hetero model?
if (type == "homo"){

    model_all <- purrr::map(level_names[-length(level_names)], run_rma1)

  } else {
  model_all <- purrr::map(level_names[-length(level_names)], run_rma2)
  }
  
  # getting estimates from intercept-less models (means for all the groups)
  res1 <- get_pred1(model, mod = mod)
  
  # getting estiamtes from all contrast models
  res2_pre <- purrr::map(model_all, ~ get_pred2(.x, mod = mod))
  
  # a list of the numbers to take out unnecessary contrasts
  contra_list <- Map(seq, from=1, to=1:(length(level_names) - 1))
  res2 <- purrr::map2_dfr(res2_pre, contra_list, ~.x[-(.y), ]) 
  # creating a table
  res_tab <- mr_results(res1, res2) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%")
  
  # results
  res_tab

}

2 PART I: LIFESPAN ANALYSIS ON LITERAURE DATA

3 Data preparation & processing

Lifespan data (literature)

#dat_full <- read_csv(here("data", "dat_07072021.csv"), na = c("", "NA")) 
#dat_full <- read_csv(here("data", "data_15022022.csv"), na = c("", "NA"))
dat_full <- read_csv(here("data", "literature", "data_05052022.csv"), na = c("", "NA"))
#glimpse(dat_full)

#loading data ####

dat_full %>% filter(is.na(Treatment_lifespan_variable) == FALSE) %>% 
  filter(Type_of_sterilization != "Vasectomy") %>% 
  mutate_if(is.character, as.factor) -> dat


#dim(dat)
#dim(dat_full)
# separating two kinds

effect_type <- ifelse(str_detect(dat$Lifespan_parameter, "Me"), "longevity", "mortality")

# splitting longevity into two types where they have SD or not
effect_type2 <- ifelse(str_detect(dat$Lifespan_parameter, "Me") & is.na(dat$Error_control_SD), "missing", effect_type)

#fix a typo in species name
dat$Species_Latin <- gsub("Macaca Fascicularis", "Macaca fascicularis", dat$Species_Latin) 
dat$Species_Latin <- gsub("Equus caballus", "Equus ferus", dat$Species_Latin)

# creating the phylo column
dat$Phylogeny <- sub(" ", "_",  dat$Species_Latin)
dat$Effect_type <- effect_type
dat$Effect_ID <- 1:nrow(dat)
# key variables
#names(dat)
#unique(dat$Species_Latin)

kable(dat, "html") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", 
    height = "250px")

Order_extracted	Study	Controlled_treatments	Type_of_sterilization	Gonads_removed	Control_treatment	Shamtreatment_moderator	Sex	Species_Latin	Species	Strain	Environment	Wild_or_semi_wild	Age_at_treatment	Maturity_at_treatment	Maturity_at_treatment_ordinal	Duration_of_treatment	Shared_control	Control_lifespan_variable	Treatment_lifespan_variable	Opposite_sex_lifespan_variable	Error_control	Error_experimental	Error_opposite_sex	Lifespan_parameter	Lifespan_unit	Error_unit	Error_control_SD	Error_experimental_SD	Error_opposite_sex_SD	Coefficent_difference_to_control	Lower_interval	Upper_interval	Coefficent_unit	Sample_size_control	Sample_size_sterilization	Sample_size_opposite_sex	Notes	Notes2	Notes3	Phylogeny	Effect_type	Effect_ID
1	Kirkpatrick and Turner 2004	No	porcine zona pellucida (PZP) immunocontraception	No	untreated	No	Female	Equus ferus	Horses	NA	Wild	Yes	2 Years	Adult	4	Less than 3 years	1	6.4700	10.2700	10.300	0.850	0.5600	0.840	Mean	years	S.E.M	5.508630	1.857310	6.285984	NA	NA	NA	NA	42	11	56	Sterilization requires booster injections and these did not receive, the longer treatment did	NA	NA	Equus_ferus	longevity	1
2	Kirkpatrick and Turner 2004	No	porcine zona pellucida (PZP) immunocontraception	No	untreated	No	Female	Equus ferus	Horses	NA	Wild	Yes	2 Years	Adult	4	More than 3 years	1	6.4700	19.9400	10.300	0.850	1.6600	0.840	Mean	years	S.E.M	5.508630	7.235772	6.285984	NA	NA	NA	NA	42	19	56	NA	NA	NA	Equus_ferus	longevity	2
3	Jacob et al 2004 A	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Rattus argentiventer	Ricefield rats	NA	Outdoor enclosure	No	Unknown - wild caught (approx 100g)	Adult (young)	4	NA	2	0.2800	0.6700	NA	6.000	34.0000	NA	Survival rate (%)	One breeding season	S.E.M	NA	NA	NA	NA	NA	NA	NA	18	6	NA	25% population sterilized data from two enclosures pooled	The impact of sterilized females on enclosed populations of ricefield rats	Estimated age at treatment from data on weight at surgery. They were approximately 100g and are never referred to as sexually immature. 1Pregnancy occurs from when the animals are 60-120 g in weight (Sudarmaji 2002).	Rattus_argentiventer	mortality	3
4	Jacob et al 2004 A	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Rattus argentiventer	Ricefield rats	NA	Outdoor enclosure	No	Unknown - wild caught (approx 100g)	Adult (young)	4	NA	3	0.1400	0.2500	NA	6.000	8.0000	NA	Survival rate (%)	One breeding season	S.E.M	NA	NA	NA	NA	NA	NA	NA	12	12	NA	50% population sterilzed data from two enclosures pooled	The impact of sterilized females on enclosed populations of ricefield rats	NA	Rattus_argentiventer	mortality	4
5	Jacob et al 2004 A	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Rattus argentiventer	Ricefield rats	NA	Outdoor enclosure	No	Unknown - wild caught (approx 100g)	Adult (young)	4	NA	4	0.2200	0.1700	NA	6.000	6.0000	NA	Survival rate (%)	One breeding season	S.E.M	NA	NA	NA	NA	NA	NA	NA	6	18	NA	75% population sterilized data from two enclosures pooled	The impact of sterilized females on enclosed populations of ricefield rats	NA	Rattus_argentiventer	mortality	5
6	Jacob et al 2004 B	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Rattus argentiventer	Ricefield rats	NA	Wild	Yes	Unknown - wild caught (approx 100g)	Adult (young)	4	NA	10	0.4100	0.4200	NA	0.110	0.1700	NA	Survival rate (%)	two months	deviance	NA	NA	NA	NA	NA	NA	NA	13	24	NA	Radiocollared - Animals spread across 4 plots giving error for survival	NA	NA	Rattus_argentiventer	mortality	6
7	Jacob et al 2004 B	Yes	Progesterone treatment	No	Untreated	No	Female	Rattus argentiventer	Ricefield rats	NA	Wild	Yes	Unknown - wild caught (approx 100g)	Adult (young)	4	NA	10	0.4100	0.4000	NA	0.110	0.0000	NA	Survival rate (%)	two months	deviance	NA	NA	NA	NA	NA	NA	NA	15	24	NA	Radiocollared -Animals spread across 4 plots giving error for survival. Progesterone treatment wore off and some females got pregnant	NA	NA	Rattus_argentiventer	mortality	7
8	Twigg et al 2000	Yes	Tubul-ligation	No	Sham surgery or no surgery	No	Female	Oryctolagus cuniculus	Rabbit	NA	Outdoor enclosure	Yes	Unknown - wild caught (see notes for age estimation)	Adult	4	NA	5	0.1330	0.4180	0.165	NA	NA	NA	Survival rate (%)	Four years	NA	NA	NA	NA	NA	NA	NA	NA	225	165	435	1993 cohorts. Assuming adults at sterilization because they do not refer to kittens, and because they show the survival of sterile females against intact females and intact adult males. They also show a plot of kitten survival and show that it is very poor	NA	NA	Oryctolagus_cuniculus	mortality	8
9	Twigg et al 2000	Yes	Tubul-ligation	No	Sham surgery or no surgery	No	Female	Oryctolagus cuniculus	Rabbit	NA	Outdoor enclosure	Yes	Unknown - wild caught - yearling. Puberty or adult?	NA	NA	NA	6	0.2390	0.3630	0.221	NA	NA	NA	Survival rate (%)	Four years	NA	NA	NA	NA	NA	NA	NA	NA	109	63	267	1994 cohorts	NA	NA	Oryctolagus_cuniculus	mortality	9
10	Twigg et al 2000	Yes	Tubul-ligation	No	Sham surgery or no surgery	No	Female	Oryctolagus cuniculus	Rabbit	NA	Outdoor enclosure	Yes	Unknown - wild caught - yearly. Puberty or adult?	NA	NA	NA	7	0.2100	0.3140	0.209	NA	NA	NA	Survival rate (%)	Four years	NA	NA	NA	NA	NA	NA	NA	NA	252	155	382	1995 cohorts - Also survival data and data split into different densities	NA	NA	Oryctolagus_cuniculus	mortality	10
11	Gipps and Jewel 1979	Yes	Castration	Yes	Sham surgery	Yes	Male	Myodes glareolus	Bank vole	NA	Outdoor enclosure	No	Immature	Prepuberty	2	NA	8	0.7820	0.9570	NA	NA	NA	NA	Survival rate (%)	6 months	NA	NA	NA	NA	NA	NA	NA	NA	23	23	NA	No reproduction in enclosure so density both treatments exposed to would be the same	NA	NA	Myodes_glareolus	mortality	11
12	Gipps and Jewel 1979	Yes	Castration	Yes	Sham surgery	Yes	Male	Myodes glareolus	Bank vole	NA	Outdoor enclosure	No	Immature	Prepuberty	2	NA	9	0.7590	1.0000	0.700	NA	NA	NA	Survival rate (%)	11 months	NA	NA	NA	NA	NA	NA	NA	NA	29	18	40	Some intacts were in a control enclosure without castrates. In the enclosure with castrates the density in the enclosure increased slightly quicker	NA	NA	Myodes_glareolus	mortality	12
13	Zakeri et al 2019	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mouse	NMRI	Laboratory	No	10 months	Adult (old)	4	NA	11	0.3600	0.5000	NA	NA	NA	NA	Survival rate (%)	11.5 months	NA	NA	NA	NA	NA	NA	NA	NA	16	16	NA	Its the sterilization treatment that is compared to two different types of control in this study	NA	NA	Mus_musculus	mortality	13
14	Zakeri et al 2019	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Mus musculus	Mouse	NMRI	Laboratory	No	10 months	Adult (old)	4	NA	11	0.3300	0.5000	NA	NA	NA	NA	Survival rate (%)	11.5 months	NA	NA	NA	NA	NA	NA	NA	NA	16	16	NA	Its the sterilization treatment that is compared to two different types of control in this study	NA	NA	Mus_musculus	mortality	14
15	Dorner 1973	Yes	Castration	Yes	Untreated	No	Male	Rattus norvegicus	Rat	Sprague-Dawley-Stammes	Laboratory	No	Day after birth	Birth	1	NA	12	570.0000	696.0000	NA	122.000	132.0000	NA	Mean	days	Standard Deviation	122.000000	132.000000	NA	NA	NA	NA	NA	12	8	NA	NA	NA	NA	Rattus_norvegicus	longevity	15
16	Asdell et al 1967	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Rattus norvegicus	Rat	Cornell Nutrion colony	Laboratory	No	Between 38-42 days	Puberty	3	NA	13	742.0000	669.0000	615.000	24.000	26.0000	21.000	Mean	days	S.E.M	169.705627	183.847763	148.492424	NA	NA	NA	NA	50	50	50	Also data for mated females but havent included as would be a different environment (e.g. With males)	NA	NA	Rattus_norvegicus	longevity	16
17	Asdell et al 1967	Yes	Castration	Yes	Intact (no surgery)	No	Male	Rattus norvegicus	Rat	Cornell Nutrion colony	Laboratory	No	Between 39-42 days	Puberty	3	NA	14	615.0000	651.0000	742.000	21.000	26.0000	24.000	Mean	days	S.E.M	148.492424	183.847763	169.705627	NA	NA	NA	NA	50	50	50	NA	NA	NA	Rattus_norvegicus	longevity	17
18	Asdell and Joshi 1976	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Rattus norvegicus	Rat	Manor-Wistar	Laboratory	No	45 days old	Puberty	3	NA	15	654.0000	844.0000	661.000	24.000	24.0000	30.000	Mean	days	S.E.M	169.705627	169.705627	212.132034	NA	NA	NA	NA	50	50	50	NA	NA	NA	Rattus_norvegicus	longevity	18
19	Asdell and Joshi 1976	Yes	Castration	Yes	Intact (no surgery)	No	Male	Rattus norvegicus	Rat	Manor-Wistar	Laboratory	No	45 days old	Puberty	3	NA	16	661.0000	775.0000	654.000	30.000	30.0000	24.000	Mean	days	S.E.M	212.132034	212.132034	169.705627	NA	NA	NA	NA	50	50	50	NA	NA	NA	Rattus_norvegicus	longevity	19
20	Arriola Apelo et al 2020	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	C57BL6	Laboratory	No	21 days	Prepuberty	2	NA	17	1006.0000	978.0000	853.000	34.300	37.4500	26.250	Median	days	S.E.M	145.522576	183.466782	136.399001	NA	NA	NA	NA	18	24	27	NA	NA	NA	Mus_musculus	longevity	20
21	Arriola Apelo et al 2020	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Mus musculus	Mice	C57BL6	Laboratory	No	21 days	Prepuberty	2	NA	18	853.0000	916.0000	1006.000	26.250	49.3600	34.300	Median	days	S.E.M	136.399001	231.518922	145.522576	NA	NA	NA	NA	27	22	18	NA	NA	NA	Mus_musculus	longevity	21
22	Benedusi et al 2015	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	C57BL76 (ERE-LucRepTOP™)	Laboratory	No	5 Months	Adult (old)	4	NA	19	0.1000	0.3500	NA	NA	NA	NA	Survival rate (%)	15 Months	NA	NA	NA	NA	NA	NA	NA	NA	20	20	NA	NA	NA	NA	Mus_musculus	mortality	22
24	Cargil et al 2003	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	CBA	Laboratory	No	21 days	Prepuberty	2	NA	21	599.2900	578.6400	NA	30.450	35.6000	NA	Median	Days	S.E.M	158.222841	178.000000	NA	NA	NA	NA	NA	27	25	NA	Extracted median lifespan from data in figure and calculated SD	NA	NA	Mus_musculus	longevity	23
25	Cox et al 2014	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Wild	Yes	Unknown - wild caught - assuming adult because caught at the same time of year as other studies, but work "adult" not specifically mentioned.	Adult	4	NA	22	0.2600	0.3300	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	168	170	NA	Raw data from Dyrad. My survival estimates from survival dont equal those extracted from the model, that probably includes covariates	NA	NA	Anolis_sagrei	mortality	24
26	Cox et al 2014	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Wild	Yes	Unknown - wild caught	Adult	4	NA	22	0.3000	0.2000	NA	NA	NA	NA	Survival rate (%)	Winter (Sept-May)	NA	NA	NA	NA	NA	NA	NA	NA	20	20	NA	Remaining animals from first mortality assessment and only controls where fat was not removed. My survival estimates from those alive after monitored period	NA	NA	Anolis_sagrei	mortality	25
27	Cox and Calsbeek 2010	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Wild	Yes	Unknown - wild caught	Adult	4	NA	23	0.0800	0.2400	NA	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	188	194	NA	Data is also available for Summer and winter mortality, seperately	NA	NA	Anolis_sagrei	mortality	26
28	Cox et al 2010	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Wild	Yes	Adult - wild caught	Adult	4	NA	24	0.2300	0.3400	NA	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	105	106	NA	NA	NA	NA	Anolis_sagrei	mortality	27
29	Reedy et al 2016	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Wild	Yes	Unknown - wild caught adult after start of breeding	Adult (young)	4	NA	25	0.2500	0.2100	0.550	NA	NA	NA	Survival rate (%)	10 weeks (of breeding season)	NA	NA	NA	NA	NA	NA	NA	NA	110	110	60	NA	NA	NA	Anolis_sagrei	mortality	28
30	Reedy et al 2016	Yes	Castration	Yes	Sham surgery	Yes	Male	Anolis sagrei	Anole lizards	NA	Wild	Yes	Unknown - wild caught adult after start of breeding	Adult (young)	4	NA	26	0.5500	0.2800	0.250	NA	NA	NA	Survival rate (%)	10 weeks (of breeding season)	NA	NA	NA	NA	NA	NA	NA	NA	60	60	110	NA	NA	NA	Anolis_sagrei	mortality	29
31	Drori and Folman 1976	Yes	Castration	Yes	Intact (no surgery)	No	Male	Rattus norvegicus	Rat	Albino	Laboratory	No	38-44 days. Stated as prepuberty	Prepuberty	2	NA	27	727.0000	817.0000	849.000	26.000	32.0000	26.000	Mean	Days	S.E.M	182.000000	224.000000	182.000000	NA	NA	NA	NA	49	49	49	Authors state that they castrated animals shortly before puberty, so coded as prepuberty	NA	NA	Rattus_norvegicus	longevity	30
32	Garratt et al 2021	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	C57BL6	Laboratory	No	7-8 weeks	Adult (young)	4	NA	28	952.0000	960.0000	956.000	20.700	36.4000	28.500	Median	Days	S.E.M	117.096883	218.400000	156.100929	NA	NA	NA	NA	32	36	30	NA	NA	NA	Mus_musculus	longevity	31
33	Hamilton et al 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Outbred	Domestic	No	Under 5 months (before sexual maturity)	Prepuberty	2	NA	29	5.3000	12.2000	7.700	0.420	1.4800	0.520	Mean	Years	S.E.M	4.136520	5.336216	4.794163	NA	NA	NA	NA	97	13	85	Correlative data is provided in a figure showing exact age at gonadectomy and lifespan for each individual	NA	NA	Felis_catus	longevity	32
34	Hamilton et al 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Outbred	Domestic	No	6 to 7 months	Puberty	3	NA	29	5.3000	8.6000	7.700	0.420	1.1200	0.520	Mean	Years	S.E.M	4.136520	5.371331	4.794163	NA	NA	NA	NA	97	23	85	NA	NA	NA	Felis_catus	longevity	33
35	Hamilton et al 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Outbred	Domestic	No	over 8 months	Adult	4	NA	29	5.3000	7.2000	7.700	0.420	0.7100	0.520	Mean	Years	S.E.M	4.136520	4.376734	4.794163	NA	NA	NA	NA	97	38	85	NA	NA	NA	Felis_catus	longevity	34
36	Hamilton et al 1969	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Outbred	Domestic	No	Various, median 6 months	NA	NA	NA	30	7.7000	8.2000	5.300	0.520	0.5200	0.420	Mean	Years	S.E.M	4.794163	4.503332	4.136520	NA	NA	NA	NA	85	75	97	NA	NA	NA	Felis_catus	longevity	35
37	Hamilton et al 1969	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Name breeds	Domestic	No	Various, median 6 months	NA	NA	NA	31	6.2000	8.2000	4.600	0.840	0.8100	0.700	Mean	Years	S.E.M	5.040000	4.723071	3.500000	NA	NA	NA	NA	36	34	25	NA	NA	NA	Felis_catus	longevity	36
38	Hamilton et al 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Name breeds	Domestic	No	Various, median 6 months	NA	NA	NA	32	4.6000	6.9000	6.200	0.700	0.5900	0.840	Mean	Years	S.E.M	3.500000	4.971428	5.040000	NA	NA	NA	NA	25	71	36	NA	NA	NA	Felis_catus	longevity	37
39	Waters et al 2011	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Rottweilers	Domestic	No	6.1-8 years	Adult (old)	4	NA	33	0.2670	1.0000	NA	NA	NA	NA	Likelyhood of exceptional longevity	Survival to 13	NA	NA	NA	NA	NA	NA	NA	NA	14	14	NA	Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data	NA	NA	Canis_lupus	mortality	38
40	Waters et al 2011	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Rottweilers	Domestic	No	2.1-6 years	Adult (young)	4	NA	33	0.2670	0.4390	NA	NA	NA	NA	Likelyhood of exceptional longevity	Survival to 13	NA	NA	NA	NA	NA	NA	NA	NA	14	57	NA	Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data	NA	NA	Canis_lupus	mortality	39
41	Waters et al 2011	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Rottweilers	Domestic	No	0.4-2 years. Estimated as puberty because rott weilers start to go through heat at approximately 12-18 months	Puberty	3	NA	33	0.2670	0.3230	NA	NA	NA	NA	Likelyhood of exceptional longevity	Survival to 13	NA	NA	NA	NA	NA	NA	NA	NA	14	65	NA	Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data	NA	NA	Canis_lupus	mortality	40
42	Holland et al 1977	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	RFM	Laboratory	No	3-4 weeks	Prepuberty	2	NA	34	638.0000	628.0000	NA	16.000	16.0000	NA	Mean	Days	S.E.M	167.044904	162.382265	NA	NA	NA	NA	NA	109	103	NA	Just used data from non-irradiated group. Lots of pathology data	NA	NA	Mus_musculus	longevity	41
43	Kirkman and Yau 1972	No	Castration	Yes	Intact (no surgery)	No	Male	Mesocricetus auratus	Hamsters	Syrian Hamsters	Laboratory	No	Unknown - not given	NA	NA	NA	35	632.0000	508.0000	543.000	NA	NA	NA	Mean	Days	NA	222.910000	151.300000	222.950000	NA	NA	NA	NA	629	72	578	Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 550-850 for intact males, 350-550 for castrated males)	NA	NA	Mesocricetus_auratus	longevity	42
44	Kirkman and Yau 1972	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mesocricetus auratus	Hamsters	Syrian Hamsters	Laboratory	No	Unknown - not given	NA	NA	NA	36	543.0000	391.0000	632.000	NA	NA	NA	Mean	Days	NA	222.950000	155.440000	222.910000	NA	NA	NA	NA	578	31	629	Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 450-750 for intact females, 250-450 for castrated females)	NA	NA	Mesocricetus_auratus	longevity	43
45	Sichuk 1965	Yes	Castration	Yes	Sham surgery	Yes	Male	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	No	6 weeks	Puberty	3	NA	37	612.0000	578.0000	589.000	NA	NA	NA	Mean	Days	NA	222.910000	151.300000	222.950000	NA	NA	NA	NA	92	90	94	Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group	NA	NA	Mesocricetus_auratus	longevity	44
46	Sichuk 1965	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	No	6 weeks	Puberty	3	NA	38	589.0000	586.0000	612.000	NA	NA	NA	Mean	Days	NA	222.950000	155.440000	222.910000	NA	NA	NA	NA	94	92	92	Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group	NA	NA	Mesocricetus_auratus	longevity	45
47	Mitchel et al 1999	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	39	131.0000	128.0000	130.000	1.400	2.7000	1.800	Mean	Months	S.E.M	50.029192	46.058550	51.951131	NA	NA	NA	NA	1277	291	833	Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death	NA	NA	Canis_lupus	longevity	46
48	Mitchel et al 1999	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	40	130.0000	144.0000	131.000	1.800	1.5000	1.400	Mean	Months	S.E.M	51.951131	40.249224	50.029192	NA	NA	NA	NA	833	720	1277	Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death	NA	NA	Canis_lupus	longevity	47
49	Moore et al 2001	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Military working dogs	Domestic	No	Various	NA	NA	NA	41	9.9700	10.4900	NA	2.100	2.0600	NA	Median	Years	Standard deviation	2.100000	2.060000	NA	NA	NA	NA	NA	641	143	NA	Shinichi made decisoin to halve the rest of N to control and the opposit sex;Do not know the sample size of castrated males. 641/927 animals in the study are intact males, the remaining animals are either castrated males or spayed females but we do not know sample size of each of these two groups.	NA	NA	Canis_lupus	longevity	48
50	Nieschlag et al 1993	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	NA	NA	No	Castrate prepubertal boys to prevent maturation of voice	Prepuberty	2	NA	42	64.3000	65.5000	NA	14.100	13.8000	NA	Mean	Years	Standard Deviation	14.100000	13.800000	NA	NA	NA	NA	NA	200	50	NA	NA	NA	NA	Homo_sapiens	longevity	49
51	Slonaker 1930	Yes	Castration	Yes	Intact (no surgery)	No	Male	Rattus norvegicus	Rat	Albino	Laboratory	No	44 days. Testes had decended by the operation	Adult (young)	4	NA	43	788.0000	770.0000	863.000	22.250	28.0000	27.690	Mean	Days	Probable error	32.987398	41.512231	41.052632	NA	NA	NA	NA	10	8	17	NA	NA	NA	Rattus_norvegicus	longevity	50
53	Slonaker 1930	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Rattus norvegicus	Rat	Albino	Laboratory	No	27.5 days	Prepuberty	2	NA	44	863.0000	755.0000	788.000	27.690	22.1500	22.250	Mean	Days	Probable error	41.052632	32.839140	32.987398	NA	NA	NA	NA	17	37	10	NA	NA	NA	Rattus_norvegicus	longevity	51
54	Slonaker 1930	Yes	Hysterectomy	No	Intact (no surgery)	No	Female	Rattus norvegicus	Rat	Albino	Laboratory	No	29 days	Prepuberty	2	NA	44	863.0000	855.0000	788.000	27.690	12.6700	22.250	Mean	Days	Probable error	41.052632	18.784285	32.987398	NA	NA	NA	NA	17	60	10	NA	NA	NA	Rattus_norvegicus	longevity	52
55	Storer et al 1982	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	RFM	Laboratory	No	50 days	Adult (young)	4	NA	45	643.4000	662.2000	NA	5.910	7.3100	NA	Mean	Days	S.E.M.	161.311607	134.193762	NA	NA	NA	NA	NA	745	337	NA	Non-irradiated controls from an irradiation experiment	NA	NA	Mus_musculus	longevity	53
56	Storer et al 1982	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	Balb/c	Laboratory	No	50 days	Adult (young)	4	NA	46	762.9000	795.5000	NA	6.210	10.9500	NA	Mean	Days	S.E.M.	179.016109	197.707397	NA	NA	NA	NA	NA	831	326	NA	Non-irradiated controls from an irradiation experiment	NA	NA	Mus_musculus	longevity	54
57	Hoffman et al 2018	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	47	10.8600	11.6400	10.860	0.110	0.0700	0.140	Mean	Years	S.E.M	3.327386	2.033618	3.685485	NA	NA	NA	NA	915	844	693	Vetcompass database	NA	NA	Canis_lupus	longevity	55
58	Hoffman et al 2018	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	48	10.8600	12.1200	10.860	0.140	0.1900	0.110	Mean	Years	S.E.M	3.685485	5.766116	3.327386	NA	NA	NA	NA	693	921	915	Vetcompass database	NA	NA	Canis_lupus	longevity	56
59	Hoffman et al 2018	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	49	8.0000	9.2100	7.680	0.070	0.0400	0.070	Mean	Years	S.E.M	8.556337	4.241509	6.018372	NA	NA	NA	NA	14941	11244	7392	VMDB - individual data for breeds available in supplementary, but just mean lifespan without error	NA	NA	Canis_lupus	longevity	57
60	Hoffman et al 2018	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	50	7.6800	9.7300	8.000	0.070	0.0400	0.070	Mean	Years	S.E.M	6.018372	5.599714	8.556337	NA	NA	NA	NA	7392	19598	14941	VMDB - individual data for breeds available in supplementary, but just mean lifespan without error	NA	NA	Canis_lupus	longevity	58
61	Mason et al 2009	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	CBA	Laboratory	No	21 days	Prepuberty	2	NA	51	727.6000	715.0000	NA	15.900	20.0000	NA	Mean	Days	S.E.M	89.943983	101.980390	NA	NA	NA	NA	NA	32	26	NA	Worked out sample size from fig 4 This and the other entry for this paper have two different control comparisons	NA	NA	Mus_musculus	longevity	59
62	Mason et al 2009	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mice	CBA	Laboratory	No	21 days	Prepuberty	2	NA	51	725.6000	715.0000	NA	20.400	20.0000	NA	Mean	Days	S.E.M	117.189078	101.980390	NA	NA	NA	NA	NA	33	26	NA	Worked out sample size from fig 4	NA	NA	Mus_musculus	longevity	60
63	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Yes	Male	Rattus norvegicus	Rats	Lewis	Laboratory	No	Birth	Birth	1	NA	52	454.0000	521.0000	484.000	18.000	27.0000	19.000	Mean	Days	S.E.M	108.000000	174.979999	123.134073	NA	NA	NA	NA	36	42	42	NA	NA	NA	Rattus_norvegicus	longevity	61
64	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Yes	Male	Rattus norvegicus	Rats	Lewis	Laboratory	No	22-28 days	Prepuberty	2	NA	52	454.0000	488.0000	484.000	18.000	28.0000	19.000	Mean	Days	S.E.M	108.000000	165.650234	123.134073	NA	NA	NA	NA	36	35	42	NA	NA	NA	Rattus_norvegicus	longevity	62
65	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Yes	Male	Rattus norvegicus	Rats	Lewis	Laboratory	No	100 days	Adult (young)	4	NA	52	454.0000	439.0000	484.000	18.000	25.0000	19.000	Mean	Days	S.E.M	108.000000	119.895788	123.134073	NA	NA	NA	NA	36	23	42	NA	NA	NA	Rattus_norvegicus	longevity	63
66	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Lewis	Laboratory	No	Birth	Birth	1	NA	53	484.0000	574.0000	454.000	19.000	33.0000	18.000	Mean	Days	S.E.M	123.134073	183.736224	108.000000	NA	NA	NA	NA	42	31	36	NA	NA	NA	Rattus_norvegicus	longevity	64
67	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Lewis	Laboratory	No	22-28 days	Prepuberty	2	NA	53	484.0000	480.0000	454.000	19.000	44.0000	18.000	Mean	Days	S.E.M	123.134073	206.378293	108.000000	NA	NA	NA	NA	42	22	36	NA	NA	NA	Rattus_norvegicus	longevity	65
68	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Lewis	Laboratory	No	100 days	Adult (young)	4	NA	53	484.0000	515.0000	454.000	19.000	41.0000	18.000	Mean	Days	S.E.M	123.134073	183.357574	108.000000	NA	NA	NA	NA	42	20	36	NA	NA	NA	Rattus_norvegicus	longevity	66
69	Tapprest et al 2017	No	Castration	Yes	Intact (no surgery)	No	Male	Equus ferus	Draught horse	NA	Farm	No	Unknown	Unknown	NA	NA	54	0.1520	0.1740	0.103	NA	NA	NA	Survival rate (%)	to 10 years	NA	NA	NA	NA	NA	NA	NA	NA	132	23	638	No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.	NA	NA	Equus_ferus	mortality	67
70	Tapprest et al 2017	No	Castration	Yes	Intact (no surgery)	No	Male	Equus ferus	Pony	NA	Farm	No	Unknown	Unknown	NA	NA	55	0.6970	0.6970	0.709	NA	NA	NA	Survival rate (%)	to 10 years	NA	NA	NA	NA	NA	NA	NA	NA	211	201	533	No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.	NA	NA	Equus_ferus	mortality	68
71	Tapprest et al 2017	No	Castration	Yes	Intact (no surgery)	No	Male	Equus ferus	Saddle horse	NA	Farm	No	Unknown	Unknown	NA	NA	56	0.5620	0.5540	0.575	NA	NA	NA	Survival rate (%)	to 10 years	NA	NA	NA	NA	NA	NA	NA	NA	1077	2203	4124	No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.	NA	NA	Equus_ferus	mortality	69
72	Hamilton 1965	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Various breeds	Domestic	No	6 -12 months for those that were known	Puberty or adult	NA	NA	57	3.2000	6.8000	7.700	0.340	0.5800	0.680	Mean	Years	S.E.M	2.741168	4.492661	5.178726	NA	NA	NA	NA	65	60	58	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	NA	NA	Felis_catus	longevity	70
73	Hamilton 1965	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Various breeds	Domestic	No	6 -12 months for those that were known	Puberty or adult	NA	NA	58	7.7000	9.2000	3.200	0.680	0.8800	0.340	Mean	Years	S.E.M	5.178726	4.656522	2.741168	NA	NA	NA	NA	58	28	65	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	NA	NA	Felis_catus	longevity	71
74	Hamilton 1965	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Various breeds	Domestic	No	6 -12 months for those that were known	Puberty or adult	NA	NA	59	6.1000	8.5000	7.400	0.660	0.5600	0.720	Mean	Years	S.E.M	4.713343	4.913980	5.091169	NA	NA	NA	NA	51	77	50	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	NA	NA	Felis_catus	longevity	72
75	Hamilton 1965	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Various breeds	Domestic	No	6 -12 months for those that were known	Puberty or adult	NA	NA	60	7.4000	8.4000	6.100	0.720	0.7100	0.660	Mean	Years	S.E.M	5.091169	4.762825	4.713343	NA	NA	NA	NA	50	45	51	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	NA	NA	Felis_catus	longevity	73
76	Huang et al 2017	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	61	9.0000	12.0000	10.000	NA	NA	NA	Median	Years	Interquartile range	5.941000	3.723000	5.947000	NA	NA	NA	NA	839	332	528	Interquartile range Intact, 5.0-13.0; castrated 9.0-14.0	NA	NA	Canis_lupus	longevity	74
77	Huang et al 2017	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	62	10.0000	12.0000	9.000	NA	NA	NA	Median	Years	Interquartile range	5.947000	3.938000	5.941000	NA	NA	NA	NA	528	607	839	Interquartile range Intact, 5.0-13.0; ovariectomy 9.7-15.0	NA	NA	Canis_lupus	longevity	75
78	Min et al 2012	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	NA	NA	No	Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.	Prepuberty	2	NA	63	55.6000	70.0000	NA	0.530	1.7600	NA	Median	Years	S.E.M	17.784639	15.840000	NA	NA	NA	NA	NA	1126	81	NA	Mok family	NA	NA	Homo_sapiens	longevity	76
79	Min et al 2012	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	NA	NA	No	Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.	Prepuberty	2	NA	63	52.9000	70.0000	NA	0.450	1.7600	NA	Median	Years	S.E.M	16.921436	15.840000	NA	NA	NA	NA	NA	1414	81	NA	Shin family	NA	NA	Homo_sapiens	longevity	77
80	Min et al 2012	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	NA	NA	No	Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.	Prepuberty	2	NA	63	50.9000	70.0000	NA	2.160	1.7600	NA	Median	Years	S.E.M	15.120000	15.840000	NA	NA	NA	NA	NA	49	81	NA	Seo family	NA	NA	Homo_sapiens	longevity	78
81	Williams et al 2007	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Unknown - wild caught (>500g weight)	Adult (young)	4	NA	65	0.2710	0.4970	0.269	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	70	143	331	1993 cohort_treatment shared between two controls	NA	NA	Oryctolagus_cuniculus	mortality	79
82	Williams et al 2007	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Unknown - wild caught (>500g weight)	Adult (young)	4	NA	65	0.2710	0.4970	0.269	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	140	143	331	1994 cohort_treatment shared between two controls	NA	NA	Oryctolagus_cuniculus	mortality	80
83	Williams et al 2007	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Yearling (>500g) Puberty or adult?	NA	NA	NA	66	0.5120	0.5160	0.271	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	41	128	280	1994 cohort_Treatment shared	NA	NA	Oryctolagus_cuniculus	mortality	81
84	Williams et al 2007	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Yearling (>500g) Puberty or adult?	NA	NA	NA	66	0.3770	0.5160	0.271	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	77	128	280	1994 cohort_Treatment shared	NA	NA	Oryctolagus_cuniculus	mortality	82
85	Williams et al 2007	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Yearling (>500g) Puberty or adult?	NA	NA	NA	67	0.2790	0.5180	0.347	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	43	114	236	1995 cohort_Treatment shared	NA	NA	Oryctolagus_cuniculus	mortality	83
86	Williams et al 2007	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Oryctolagus cuniculus	Rabbits	European Rabbit	Wild	Yes	Yearling (>500g) Puberty or adult?	NA	NA	NA	67	0.3380	0.5180	0.347	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	68	114	236	1995 cohort_Treatment shared	NA	NA	Oryctolagus_cuniculus	mortality	84
87	Urfer et al 2019	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	68	14.0900	14.1500	13.770	0.033	0.0130	0.046	Median	years	95% Confidence intervals	18.753700	12.400487	22.107487	NA	0.033	0.0130	0.046	322958	909894	230974	95% confidence intervals Intact Male: 14.03-14.16; Castrated male: 14.13-14.18	NA	NA	Canis_lupus	longevity	85
88	Urfer et al 2019	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	69	13.7700	14.3500	14.090	0.046	0.0102	0.033	Median	years	95% Confidence intervals	22.107487	9.710121	18.753700	NA	0.046	0.0102	0.033	230974	906252	322958	95% confidence intervals Intact female: 13.68-13.86, OVX female: 14.33-14.37	NA	NA	Canis_lupus	longevity	86
89	Ramsey 2005	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Trichosurus vulpecula	Possum	NA	Wild	Yes	Various	Adult	4	NA	70	0.7800	0.8700	NA	NA	NA	NA	Survival rate (%)	Annual (taken from 4 years)	NA	NA	NA	NA	NA	NA	NA	NA	56	56	NA	Orongorono 50% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999	NA	NA	Trichosurus_vulpecula	mortality	87
90	Ramsey 2005	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Trichosurus vulpecula	Possum	NA	Wild	Yes	Various	Adult	4	NA	71	0.7200	0.8300	NA	NA	NA	NA	Survival rate (%)	Annual (taken from 4 years)	NA	NA	NA	NA	NA	NA	NA	NA	36	142	NA	Orongorono 80% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999	NA	NA	Trichosurus_vulpecula	mortality	88
91	Ramsey 2005	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Trichosurus vulpecula	Possum	NA	Wild	Yes	Various	Adult	4	NA	72	0.6300	0.7600	NA	NA	NA	NA	Survival rate (%)	Annual (taken from 4 years)	NA	NA	NA	NA	NA	NA	NA	NA	215	215	NA	Turitea 50% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999. Confidence intervals are provided	NA	NA	Trichosurus_vulpecula	mortality	89
92	Ramsey 2005	Yes	Tubul-ligation	No	Sham surgery	Yes	Female	Trichosurus vulpecula	Possum	NA	Wild	Yes	Various	Adult	4	NA	73	0.6000	0.7400	NA	NA	NA	NA	Survival rate (%)	Annual (taken from 4 years)	NA	NA	NA	NA	NA	NA	NA	NA	31	123	NA	Turitea 80% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999	NA	NA	Trichosurus_vulpecula	mortality	90
93	Ramsey et al 2021	Yes	levonorgestrel implant	No	Sham capture	Yes	Female	Phascolarctos cinereus	Koala	NA	Wild	Yes	Mature females, as definted by toothwear, under 1 year	Adult (young)	4	NA	74	0.7200	0.7800	NA	NA	NA	NA	Survival rate (%)	Annual (average from across all years)	NA	NA	NA	NA	NA	NA	NA	NA	603	4355	NA	Survival rate taken as the average across all years. Sample size is also from across all years. Yearly data is also available in supplementary. Confidence intervals are provided.	NA	NA	Phascolarctos_cinereus	mortality	91
94	Muhlock 1959	Yes	Castration	Yes	Intact (no surgery)	No	Male	Mus musculus	Mouse	DBA	Laboratory	No	Weaning (1month)	Prepuberty	2	NA	75	578.0000	595.0000	667.000	10.120	11.4400	9.570	Mean	days	S.E.M	78.389183	102.322471	88.748529	NA	NA	NA	NA	60	80	86	Extracted data and calculated mean and SE from graph	NA	NA	Mus_musculus	longevity	92
95	Muhlock 1959	Yes	Ovariectomy	Yes	Intact (no surgery)	No	Female	Mus musculus	Mouse	DBA	Laboratory	No	Weaning (1 month)	Prepuberty	2	NA	76	667.0000	627.0000	578.000	9.570	10.6700	10.120	Mean	days	S.E.M	88.748529	87.987074	78.389183	NA	NA	NA	NA	86	68	60	Extracted data and calculated mean and SE from graph	NA	NA	Mus_musculus	longevity	93
96	Jewel 1997	Yes	Castration	Yes	Intact (no surgery)	No	Male	Ovis aries	Sheep	Soay sheep	Wild	Yes	Lambs	Birth	1	NA	77	0.3600	0.7100	0.410	NA	NA	NA	Survival rate (%)	one year	NA	NA	NA	NA	NA	NA	NA	NA	14	14	54	1978 Calaculated the survival rate to the timepoint nearest 50% intact male survival	NA	NA	Ovis_aries	mortality	94
97	Jewel 1997	Yes	Castration	Yes	Intact (no surgery)	No	Male	Ovis aries	Sheep	Soay sheep	Wild	Yes	Lambs	Birth	1	NA	78	0.2000	0.8800	0.910	NA	NA	NA	Survival rate (%)	One year	NA	NA	NA	NA	NA	NA	NA	NA	8	5	44	1979	NA	NA	Ovis_aries	mortality	95
98	Jewel 1997	Yes	Castration	Yes	Intact (no surgery)	No	Male	Ovis aries	Sheep	Soay sheep	Wild	Yes	Lambs	Birth	1	NA	79	0.0800	0.6600	0.400	NA	NA	NA	Survival rate (%)	Five years	NA	NA	NA	NA	NA	NA	NA	NA	50	50	83	1980. Calculated the survival rate to timepoint nearest to 50% intact male survival, and where there is data for all groups. A survival curve is also available but there is alot of missing data	NA	NA	Ovis_aries	mortality	96
99	Iwasa et al 2018	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	23 weeks	Late adult	4	NA	80	0.4300	0.8600	NA	NA	NA	NA	Survival rate (%)	~85 weeks.	NA	NA	NA	NA	NA	NA	NA	NA	8	7	NA	Calculated from a partial survival curve. Looked at when 50% of the control group died and then the number alive in the treatment group at this point	NA	NA	Rattus_norvegicus	mortality	97
100	Hamilton and Mestler 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	Mentally handicaped individuals	NA	No	8-14 years (prepubertal)	Prepuberty	2	NA	81	64.7000	76.3000	NA	0.990	1.3600	NA	Median lifespan (for those alive at 40)	Years	S.E.M	17.709658	5.769991	NA	NA	NA	NA	NA	320	18	NA	Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages	NA	NA	Homo_sapiens	longevity	98
101	Hamilton and Mestler 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	Mentally handicaped individuals	NA	No	15-19 years	Adult (young)	4	NA	81	64.7000	72.9000	NA	0.990	5.1300	NA	Median lifespan (for those alive at 40)	Years	S.E.M	17.709658	43.529493	NA	NA	NA	NA	NA	320	72	NA	Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages	NA	NA	Homo_sapiens	longevity	99
102	Hamilton and Mestler 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	Mentally handicaped individuals	NA	No	20-29 years	Adult (young)	4	NA	81	64.7000	69.6000	NA	0.990	2.5000	NA	Median lifespan (for those alive at 40)	Years	S.E.M	17.709658	19.525624	NA	NA	NA	NA	NA	320	61	NA	Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages	NA	NA	Homo_sapiens	longevity	100
103	Hamilton and Mestler 1969	No	Castration	Yes	Intact (no surgery)	No	Male	Homo sapiens	Humans	Mentally handicaped individuals	NA	No	30-39 years	Adult (old)	4	NA	81	64.7000	68.9000	NA	0.990	2.0500	NA	Median lifespan (for those alive at 40)	Years	S.E.M	17.709658	14.350000	NA	NA	NA	NA	NA	320	49	NA	Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages	NA	NA	Homo_sapiens	longevity	101
104	Hamilton and Mestler 1969	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	Mentally handicaped individuals	NA	No	13-46 years old	Adult	4	NA	82	33.9000	56.2000	NA	1.360	4.6900	NA	Median	Years	S.E.M	15.265962	15.554970	NA	NA	NA	NA	NA	126	11	NA	Only one female was 13 years and all the rest were clearly adult so group coded as adult	NA	NA	Homo_sapiens	longevity	102
105	Oneil et al 2013	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	83	11.9900	11.9900	11.590	NA	NA	NA	Mean	Years	Estimated	NA	NA	NA	0.8	0.500	1.1000	Average difference in years to control	1464	1224	1082	Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated	NA	NA	Canis_lupus	longevity	103
106	Oneil et al 2013	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	84	11.5900	12.3900	11.990	NA	NA	NA	Mean	Years	Estimated	NA	NA	NA	Look at comments for Coefficent whiich is in relation to the control female from the same study)	NA	NA	NA	1082	1304	1464	Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated	NA	NA	Canis_lupus	longevity	104
107	Oneil et al 2015	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Various breeds	Domestic	No	Various	NA	NA	NA	85	12.8100	14.7100	14.610	NA	NA	NA	Mean	Years	Estimated	NA	NA	NA	0.6	0.100	1.0000	Average difference in years to control	704	1296	707	Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated	NA	NA	Felis_catus	longevity	105
108	Oneil et al 2015	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Various breeds	Domestic	No	Various	NA	NA	NA	86	14.6100	15.2100	12.810	NA	NA	NA	Mean	Years	Estimated	NA	NA	NA	Look at comments for Coefficent whiich is in relation to the control female from the same study)	NA	NA	NA	707	1302	704	Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated	NA	NA	Felis_catus	longevity	106
112	Wilson et al 2019	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Under 50 years old	Adult	4	NA	90	0.9351	0.9166	NA	NA	NA	NA	Survival rate (%)	21.5 years (median follow-up)	NA	NA	NA	NA	NA	NA	NA	NA	10218	851	NA	Calculated survival rate from those surviving across the study period. Hazard ratios are also provided in the paper, and additional analysis cotrolling for factors. There is also analysis where women are seperated according to whether they have used hormone replacement therapy. Additional studies are cited that have conducted this type of analysis.	NA	NA	Homo_sapiens	mortality	107
113	Wilson et al 2019	No	Hysterectomy	No	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Under 50 years old	Adult	4	NA	90	0.9351	0.9324	NA	NA	NA	NA	Survival rate (%)	21.5 years (median follow-up)	NA	NA	NA	NA	NA	NA	NA	NA	10218	2472	NA	Calculated survival rate from those surviving across the study period. Hazard ratios are also provided in the paper, and additional analysis cotrolling for factors. There is also analysis where women are seperated according to whether they have used hormone replacement therapy. Additional studies are cited that have conducted this type of analysis.	NA	NA	Homo_sapiens	mortality	108
114	Cheng 2019	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	UMHet3	Laboratory	No	Under 30 days is stated. The figure shows weights starting at approximately 15-20 days so this is used in the correlation analysis	Prepuberty	2	NA	91	0.8100	0.9700	NA	NA	NA	NA	Survival rate (%)	500 days	NA	NA	NA	NA	NA	NA	NA	NA	238	238	NA	NA	NA	NA	Mus_musculus	mortality	109
115	Bronson 1981	No	Castration	Yes	Intact (no surgery)	No	Male	Felis catus	Cats	Various breeds	Domestic	No	After 6 months (they state few were done before neutered before 6 months or so)	Puberty or adult	NA	NA	92	4.9700	7.3400	6.650	3.660	4.2900	5.660	Mean	Years - for cats surviving to two years old	Standard deviation	3.660000	4.290000	5.660000	NA	NA	NA	NA	219	265	99	Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.	NA	NA	Felis_catus	longevity	110
116	Bronson 1981	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Felis catus	Cats	Various breeds	Domestic	No	After 6 months (they state few were done before neutered before 6 months or so)	Puberty or adult	NA	NA	93	6.6500	9.1100	4.970	5.660	5.1300	3.660	Mean	Years - for cats surviving to two years old	Standard deviation	5.660000	5.130000	3.660000	NA	NA	NA	NA	99	220	219	Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.	NA	NA	Felis_catus	longevity	111
117	Bronson 1982	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	94	8.0000	9.9000	7.700	5.200	6.8000	4.400	Mean (from those alive after 2)	Years	Standard Deviation	5.200000	6.800000	4.400000	NA	NA	NA	NA	755	54	224	Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison	NA	NA	Canis_lupus	longevity	112
118	Bronson 1982	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Various breeds	Domestic	No	Various	NA	NA	NA	95	7.7000	8.8000	8.000	4.400	6.9000	5.200	Mean (from those alive after 2)	Years	Standard Deviation	4.400000	6.900000	5.200000	NA	NA	NA	NA	224	528	755	Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison	NA	NA	Canis_lupus	longevity	113
119	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site FP predation natural	Yes	Unknown	NA	NA	NA	96	0.3590	0.5250	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	78	80	NA	Site FP predation natural	NA	NA	Anolis_sagrei	mortality	114
120	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site FC no predation	No	Unknown	NA	NA	NA	97	0.5430	0.3590	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	81	78	NA	Site FC no predation. Included as not wild-semi wild because protected from predation	NA	NA	Anolis_sagrei	mortality	115
121	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site HC Bird predation	Yes	Unknown	NA	NA	NA	98	0.3210	0.3920	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	81	79	NA	Site HC Bird predation	NA	NA	Anolis_sagrei	mortality	116
122	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site FP Natural	Yes	Unknown	NA	NA	NA	99	0.3270	0.5610	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	110	114	NA	Site FP Natural	NA	NA	Anolis_sagrei	mortality	117
123	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site NC No predation	No	Unknown	NA	NA	NA	100	0.2930	0.3470	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	75	75	NA	Site NC No predation. Included as not wild semi-wild because protected from predation	NA	NA	Anolis_sagrei	mortality	118
124	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site FC Bird predation	Yes	Unknown	NA	NA	NA	101	0.4930	0.5730	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	75	75	NA	Site FC Bird predation	NA	NA	Anolis_sagrei	mortality	119
125	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site HC Bird and snake predation	Yes	Unknown	NA	NA	NA	102	0.3330	0.2840	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	74	75	NA	Site HC Bird and snake predation	NA	NA	Anolis_sagrei	mortality	120
126	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site Mc Bird and snake predation	Yes	Unknown	NA	NA	NA	103	0.2670	0.2530	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	75	75	NA	Site Mc Bird and snake predation	NA	NA	Anolis_sagrei	mortality	121
127	Cox et al 2021	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Anolis sagrei	Anole lizards	NA	Site FP natural	Yes	Unknown	NA	NA	NA	104	0.2290	0.3400	NA	NA	NA	NA	Survival rate (%)	One breeding season (May-Sept)	NA	NA	NA	NA	NA	NA	NA	NA	105	106	NA	Site FP natural	NA	NA	Anolis_sagrei	mortality	122
128	Skinner 2007	Yes	Tubul-ligation	No	Anesthetized but no surgery	Yes	Female	Odocoileus virginianus	White-tailed deer	NA	Suburban chicago	No	Unknown	NA	NA	NA	105	0.7500	0.5200	0.660	NA	NA	NA	Survival rate (%)	four years	NA	NA	NA	NA	NA	NA	NA	NA	34	67	79	It is stated that more treatment does died from vechicle accidents (e.g. Human biased)	NA	NA	Odocoileus_virginianus	mortality	123
129	Kent et al 2018	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	Golden retriver	Domestic	No	Unknown	NA	NA	NA	106	8.6800	9.3500	5.890	NA	NA	NA	Median	Years	Range	3.230000	2.710000	3.270000	NA	NA	NA	NA	118	228	58	Standard deviation calculated from the range according to Wan et al 2014	NA	NA	Canis_lupus	longevity	124
130	Kent et al 2018	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Canis lupus	Dogs	Golden retriver	Domestic	No	Unknown	NA	NA	NA	107	5.8900	9.5100	8.680	NA	NA	NA	Median	Years	Range	3.270000	2.740000	3.230000	NA	NA	NA	NA	58	248	118	Standard deviation calculated from the range according to Wan et al 2014	NA	NA	Canis_lupus	longevity	125
131	Iversen et al 2007	No	Tubul-ligation	No	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Various	NA	NA	NA	108	0.9195	0.9124	NA	NA	NA	NA	Survival rate (%)	1968-2004 approx	NA	NA	NA	NA	NA	NA	NA	NA	2634	2511	NA	Groups differ in some demographic factors ie parity. Used data from Table 3 (all cause dealth), where women who had a history of cancer or cardiovascular disease etc, before their operation or follow-up period, were excluded from the analysis (see statistics section).	NA	NA	Homo_sapiens	mortality	126
132	Sato et al 1997	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	9 months	Adult	4	NA	109	0.8000	0.9710	NA	NA	NA	NA	Survival rate (%)	6 months	NA	NA	NA	NA	NA	NA	NA	NA	35	35	NA	NA	NA	NA	Rattus_norvegicus	mortality	127
133	Aida et al 1984	Yes	Castration	Yes	Sham surgery	Yes	Male	Oncorhynchus masou	masu salmon	NA	Laboratory	No	precocious	NA	NA	NA	110	0.3360	0.6200	NA	NA	NA	NA	Survival rate (%)	8 months	NA	NA	NA	NA	NA	NA	NA	NA	107	316	NA	Experiments were conducted on precocious mature males. Need to work out how to classify this in terms of maturity at castration. Some males had partial gonads remaining at	NA	NA	Oncorhynchus_masou	mortality	128
135	Pullinger and Head 1964	Yes	Ovariectomy	Yes	Untreated	No	Female	Mus musculus	Mice	C3Hf	Laboratory	No	56-111 days of age	Adult	4	NA	112	0.3700	0.2300	NA	NA	NA	NA	Survival rate (%)	to 30 months	NA	NA	NA	NA	NA	NA	NA	NA	114	40	NA	"average lifespan" in months is also provided but no error. Calculated the percentage surviving to 30 months as dead ranges are provided in 6 brackets and this is the closest to the median for control females. This group is control virgin females (OVX data was shared and compared to breeding females in the other female comparison for this paper but I removed it because I dont think social environment was comparable)	NA	NA	Mus_musculus	mortality	129
136	Robertson et al 1961	No	Castration	Yes	Untreated	No	Male	Oncorhynchus nerka	Kokanee salmon	NA	Experimental pond	No	2 years 1 month	Prior to sexual maturity	2	NA	113	4.0500	5.3100	4.260	NA	NA	NA	Mean	NA	NA	0.590000	1.600000	0.550000	NA	NA	NA	NA	41	13	58	Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.	NA	NA	Oncorhynchus_nerka	longevity	130
137	Robertson et al 1961	No	Ovariectomy	Yes	Untreated	No	Female	Oncorhynchus nerka	Kokanee salmon	NA	Experimental pond	No	2 years 1 month	Prior to sexual maturity	2	NA	114	4.2600	5.8900	4.050	NA	NA	NA	Mean	NA	NA	0.550000	1.630000	0.590000	NA	NA	NA	NA	58	16	41	Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.	NA	NA	Oncorhynchus_nerka	longevity	131
138	Saunders et al 2002	Yes	Tubul-ligation	No	Intact (no surgery)	No	Female	Vulpes vulpes	Foxes	NA	Wild	Yes	Adult (toothwear)	NA	4	NA	115	0.8300	0.8500	0.885	NA	NA	NA	Survival rate (%)	2 years	NA	NA	NA	NA	NA	NA	NA	NA	6	20	26	Included as controled because comparison is to animals on the same site. Male data is also from the same site	NA	NA	Vulpes_vulpes	mortality	132
139	Saunders et al 2002	No	Tubul-ligation	No	Sham surgery	Yes	Female	Vulpes vulpes	Foxes	NA	Wild	Yes	Adult (toothwear)	NA	4	NA	115	0.7860	0.8500	0.955	NA	NA	NA	Survival rate (%)	2 years	NA	NA	NA	NA	NA	NA	NA	NA	14	20	22	Included as not controlled as effect of sham surgery was assessed on a neighouring site although the authors compare survivalship rates between	NA	NA	Vulpes_vulpes	mortality	133
140	Collins and Kasbohn 2017	No	Ovariectomy	Yes	untreated	No	Female	Equus ferus	Feral horse	NA	Wild	Yes	Adult	NA	4	NA	116	0.8610	0.8510	0.900	NA	NA	NA	Survival rate (%)	Annual	NA	NA	NA	NA	NA	NA	NA	NA	114	36	10	There is also male data but they used a lot of different methods, mainly vasectomy and chemical castration, and data is not split according to surgery type	NA	NA	Equus_ferus	mortality	134
141	Urfer et al 2020	No	Castration	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	NA	Various	No	Various	NA	NA	NA	117	15.0000	15.2000	14.100	NA	NA	NA	Median survival time	Years	Confidence interval (calcualted SD)	17.598000	16.530000	20.090000	NA	NA	NA	NA	2115	8567	1551	Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included	NA	NA	Canis_lupus	longevity	135
142	Urfer et al 2020	No	Ovariectomy	Yes	Intact (no surgery)	No	Male	Canis lupus	Dogs	NA	Various	No	Various	NA	NA	NA	118	14.1000	15.8000	15.000	NA	NA	NA	Median survival time	Years	Confidence interval (calcualted SD)	20.090000	16.670000	17.598000	NA	NA	NA	NA	1551	8711	2115	Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included	NA	NA	Canis_lupus	longevity	136
143	Ossewaarde et al 2005	No	Hysterectomy	No	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Various	NA	NA	NA	119	0.7825	0.8197	NA	NA	NA	NA	Survival rate (%)	Mean 17 years	NA	NA	NA	NA	NA	NA	NA	NA	10087	743	NA	Taken from number of cases of mortality across the follow-up period (Table 3)	NA	NA	Homo_sapiens	mortality	137
144	Ossewaarde et al 2005	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Various	NA	NA	NA	119	0.7825	0.8139	NA	NA	NA	NA	Survival rate (%)	Mean 17 years	NA	NA	NA	NA	NA	NA	NA	NA	10087	865	NA	Taken from number of cases of mortality across the follow-up period (Table 3)	NA	NA	Homo_sapiens	mortality	138
145	Hotchkiss 1995	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	90 days	Adult	4	NA	120	0.3330	0.8330	NA	NA	NA	NA	Survival rate (%)	to 630 days	NA	NA	NA	NA	NA	NA	NA	NA	12	12	NA	Survival to 630 days. Animals that presented with subquaneous tumors had these surgically removed.	NA	NA	Rattus_norvegicus	mortality	139
146	Rocca et al 2006	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Under 45 years old	Adult	4	NA	121	0.8380	0.7340	NA	NA	NA	NA	Survival rate (%)	Not sure, but total follow up years is provided in tables	NA	NA	NA	NA	NA	NA	NA	NA	1417	124	NA	Data from Table 1 and includes all individuals in that bracket	NA	NA	Homo_sapiens	mortality	140
147	Rocca et al 2006	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	NA	Adult	4	NA	122	0.6280	0.6910	NA	NA	NA	NA	Survival rate (%)	Not sure, but total follow up years is provided in tables	NA	NA	NA	NA	NA	NA	NA	NA	645	243	NA	Data from Table 1 and includes all individuals in that bracket	NA	NA	Homo_sapiens	mortality	141
148	Rocca et al 2006	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	NA	Adult	4	NA	123	0.5050	0.5530	NA	NA	NA	NA	Survival rate (%)	Not sure, but total follow up years is provided in tables	NA	NA	NA	NA	NA	NA	NA	NA	321	170	NA	Data from Table 1 and includes all individuals in that bracket	NA	NA	Homo_sapiens	mortality	142
149	Howard et al 2005	No	Hysterectomy	No	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	NA	Adult	4	NA	124	0.9790	0.9770	NA	NA	NA	NA	Survival rate (%)	NA	NA	NA	NA	NA	NA	NA	NA	NA	52976	18687	NA	Total sample size split between groups is shown in "age at screening", which equates to the total sample size given at start of results. Calculated mortality by adding all causes together. Not sure have calculated this correctly or why it differs so dramatically from % annual that is given	NA	NA	Homo_sapiens	mortality	143
150	Howard et al 2005	No	Ovariectomy	Yes	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	NA	Adult	4	NA	124	0.9790	0.9760	NA	NA	NA	NA	Survival rate (%)	NA	NA	NA	NA	NA	NA	NA	NA	NA	52976	18251	NA	Total sample size split between groups is shown in "age at screening", which equates to the total sample size given at start of results. Calculated mortality by adding all causes together. Not sure have calculated this correctly or why it differs so dramatically from % annual that is given	NA	NA	Homo_sapiens	mortality	144
151	Phelan 1995	Yes	Ovariectomy	Yes	Sham surgery	Yes	Female	Mus musculus	Mice	Swiss	Laboratory	No	Weaning	Weaning	2	NA	125	0.4100	0.6200	NA	NA	NA	NA	Survival rate (%)	To 800 days	NA	NA	NA	NA	NA	NA	NA	NA	30	30	NA	Animals were maintained on a 90% of adlibitum diet. This was a control group for a sepeate CR study and they state stopped the animals getting fat.	NA	NA	Mus_musculus	mortality	145
152	Manson et al 2013	No	Hysterectomy	No	Intact (no surgery)	No	Female	Homo sapiens	Humans	NA	NA	No	Various	Adult	4	NA	126	0.9706	0.9449	NA	NA	NA	NA	Survival rate (%)	17 years	NA	NA	NA	NA	NA	NA	NA	NA	8102	5429	NA	The control and hysterectomy data is taken from the placebo of two parrellel studies run by the WHI. Women in both studies were matched for age and various variables, although some differences between the cohorts were present as would be typical.	NA	NA	Homo_sapiens	mortality	146
153	Deleuze et al 2021	No	Tubectomy	No	Intact (no surgery)	No	Female	Macaca fascicularis	Long-tailed Macaques	NA	NA	Yes	Adult (most) and a few subadult	NA	4	NA	127	0.8600	0.8700	NA	NA	NA	NA	Survival rate (%)	3 years	NA	NA	NA	NA	NA	NA	NA	NA	22	39	NA	2017-2018 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.	NA	NA	Macaca_fascicularis	mortality	147
154	Deleuze et al 2021	No	Tubectomy	No	Intact (no surgery)	No	Female	Macaca fascicularis	Long-tailed Macaques	NA	NA	Yes	Adult (most) and a few subadult	NA	4	NA	128	0.9300	0.9500	NA	NA	NA	NA	Survival rate (%)	Annual	NA	NA	NA	NA	NA	NA	NA	NA	41	43	NA	2018-2019 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.	NA	NA	Macaca_fascicularis	mortality	148
155	Deleuze et al 2021	No	Tubectomy	No	Intact (no surgery)	No	Female	Macaca fascicularis	Long-tailed Macaques	NA	NA	Yes	Adult (most) and a few subadult	NA	4	NA	129	0.9500	0.9800	NA	NA	NA	NA	Survival rate (%)	Annual	NA	NA	NA	NA	NA	NA	NA	NA	134	45	NA	2019-2020 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.	NA	NA	Macaca_fascicularis	mortality	149
156	Wang et al 2021	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	C57BL6	NA	No	8 months	Adult	4	NA	130	933.5000	938.5000	NA	NA	NA	NA	Median	NA	SD	129.000000	117.000000	NA	NA	NA	NA	NA	22	22	NA	Extended figure 3I	NA	NA	Mus_musculus	longevity	150
157	Wang et al 2021	Yes	Castration	Yes	Sham surgery	Yes	Male	Mus musculus	Mice	NA	NA	No	18 months	Adult	4	NA	131	974.0000	959.0000	NA	NA	NA	NA	Median	NA	SD	95.000000	93.000000	NA	NA	NA	NA	NA	19	19	NA	Fig 6q Animals had been injected with a control shRNA	NA	NA	Mus_musculus	longevity	151
158	Tidiere 2016	No	Various	NA	Intact (no surgery)	No	Female	Varecia rubra	Red ruffed lemur	NA	Zoo	No	Unknown	Unknown	NA	NA	132	15.6500	19.8400	16.180	NA	NA	NA	Mean	NA	SD	12.454792	10.210810	13.436925	NA	NA	NA	NA	689	67	927	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	NA	NA	Varecia_rubra	longevity	152
159	Tidiere 2016	No	Various	NA	Intact (no surgery)	No	Male	Varecia rubra	Red ruffed lemur	NA	Zoo	No	Unknown	Unknown	NA	NA	133	16.1800	19.4000	15.650	NA	NA	NA	Mean	NA	SD	13.436925	11.495374	12.454792	NA	NA	NA	NA	927	38	689	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	NA	NA	Varecia_rubra	longevity	153
160	Tidiere 2016	No	Various	NA	Intact (no surgery)	No	Female	Varecia variegata	Black and white ruffed lemur	NA	Zoo	No	Unknown	Unknown	NA	NA	134	13.9500	18.0300	13.820	NA	NA	NA	Mean	NA	SD	13.122827	12.489796	14.485185	NA	NA	NA	NA	1542	36	1999	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	NA	NA	Varecia_variegata	longevity	154
161	Tidiere 2016	No	Various	NA	Intact (no surgery)	No	Male	Varecia variegata	Black and white ruffed lemur	NA	Zoo	No	Unknown	Unknown	NA	NA	134	13.8200	15.8600	13.950	NA	NA	NA	Mean	NA	SD	14.485185	12.320698	13.122827	NA	NA	NA	NA	1999	37	1542	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	NA	NA	Varecia_variegata	longevity	155
162	Larsen1969	Yes	Gonadectomy	Yes	Intact (no surgery)	No	Female	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	No	Prior to sexual maturity - Jan	NA	2	NA	135	0.1000	0.3000	NA	NA	NA	NA	Survival rate (%)	May (after spawning)	NA	NA	NA	NA	NA	NA	NA	NA	10	10	10	Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct.	NA	NA	Lamperta_fluviatilis	mortality	156
163	Larsen1969	Yes	Gonadectomy	Yes	Intact (no surgery)	No	Male	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	No	Prior to sexual maturity - Jan	NA	2	NA	136	0.1000	0.2700	NA	NA	NA	NA	Survival rate (%)	May (after spawning)	NA	NA	NA	NA	NA	NA	NA	NA	10	11	10	Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct	NA	NA	Lamperta_fluviatilis	mortality	157
164	Larsen 1973	Yes	Gonadectomy	Yes	Intact (no surgery)	No	Female	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	No	Prior to sexual maturity - Either Jan or October prior year	NA	2	NA	137	0.0600	0.2500	NA	NA	NA	NA	Survival rate (%)	May (after spawning)	NA	NA	NA	NA	NA	NA	NA	NA	17	20	50	Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 11. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct	NA	NA	Lamperta_fluviatilis	mortality	158
165	Larsen 1973	Yes	Gonadectomy	Yes	Intact (no surgery)	No	Male	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	No	Prior to sexual maturity - Either Jan or October prior year	NA	2	NA	138	0.0200	0.2500	NA	NA	NA	NA	Survival rate (%)	May (after spawning)	NA	NA	NA	NA	NA	NA	NA	NA	50	20	17	Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 12, pooled for the two treatment times. Sample size for the controls comes from fig 4 where heart weight is given just for the same year cohort and sample.sizes are shown. It is started that they have studied approximately 200 animals over the 4 years and never seen a control live much past spawning but this gives a definitive value for that year. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct	NA	NA	Lamperta_fluviatilis	mortality	159

Calculating effect size for the main data

# let's get CVs

dat %>% group_by(Study) %>% summarise(cv2_cont = mean((Error_control_SD/Control_lifespan_variable)^2, na.rm = T), cv2_trt = mean((Error_experimental_SD/Treatment_lifespan_variable)^2, na.rm = T), cv2_opst = mean((Error_opposite_sex_SD/Opposite_sex_lifespan_variable)^2, na.rm = T), n_cont = mean(Sample_size_control, na.rm = T), n_trt =  mean(Sample_size_sterilization, na.rm = T), n_opst =  mean(Sample_size_opposite_sex, na.rm = T)) %>% 
  ungroup() %>% 
  summarise(cv2_cont = weighted.mean(cv2_cont, n_cont, na.rm = T), cv2_trt = weighted.mean(cv2_trt, n_trt, na.rm = T), cv2_opst = weighted.mean(cv2_opst, n_opst, na.rm = T)) -> cvs

# lnRR

# survival proportion using arcsin transformation = lnrrp
# using CV
dat$yi <- ifelse(effect_type == "longevity", lnrrm(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[1]], lnrrp(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control)[[1]])

dat$vi <- ifelse(effect_type == "longevity", lnrrm(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[2]], lnrrp(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control)[[2]])

# here vi2 is the same as vi1
dat$vi2 <- ifelse(effect_type2 == "missing", lnrrm(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[2]], 
                  ifelse(effect_type2 == "longevity", 
                  lnrrm2(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, dat$Error_control_SD, dat$Error_experimental_SD)[[2]],                                                                         lnrrp(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization,dat$Sample_size_control)[[2]]))

# survival transformation using -1/log(p)
# first transform - just to 
pos <- which(dat$Treatment_lifespan_variable == 1)
Treatment_lifespan_variable2 <- replace(dat$Treatment_lifespan_variable, pos, 0.99)
dat$Treatment_lifespan_variable2 <-Treatment_lifespan_variable2

dat$y2 <- ifelse(effect_type == "longevity", lnrrm(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[1]], lnrre(dat$Treatment_lifespan_variable2, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control)[[1]])

dat$v2 <- ifelse(effect_type == "longevity", lnrrm(dat$Treatment_lifespan_variable, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[2]], lnrre(dat$Treatment_lifespan_variable2, dat$Control_lifespan_variable, dat$Sample_size_sterilization, dat$Sample_size_control)[[2]])



# getting effect size for the long format 
# we create a longer data format

dat1 <- dat
dat2 <- dat

dat1$yi <- ifelse(effect_type == "longevity", 
                  lnrrm(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable,  
                        dat$Sample_size_control, dat$Sample_size_opposite_sex, 
                        cvs[["cv2_cont"]],cvs[["cv2_opst"]])[[1]], 
                  lnrrp(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
                        dat$Sample_size_control,  dat$Sample_size_opposite_sex)[[1]])

dat1$vi <-ifelse(effect_type == "longevity", 
                 lnrrm(dat$Control_lifespan_variable,dat$Opposite_sex_lifespan_variable,  
                       dat$Sample_size_control, dat$Sample_size_opposite_sex, 
                       cvs[["cv2_cont"]],cvs[["cv2_opst"]])[[2]], 
                 lnrrp(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
                       dat$Sample_size_control,  dat$Sample_size_opposite_sex)[[2]])

# dat1$y2 <- ifelse(effect_type == "longevity", 
#                   lnrrm(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable,  
#                         dat$Sample_size_control, dat$Sample_size_opposite_sex, 
#                         cvs[["cv2_cont"]],cvs[["cv2_opst"]])[[1]], 
#                   lnrre(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
#                         dat$Sample_size_control,  dat$Sample_size_opposite_sex)[[1]])
# 
# dat1$v2 <-ifelse(effect_type == "longevity", 
#                  lnrrm(dat$Control_lifespan_variable,dat$Opposite_sex_lifespan_variable,  
#                        dat$Sample_size_control, dat$Sample_size_opposite_sex, 
#                        cvs[["cv2_cont"]],cvs[["cv2_opst"]])[[2]], 
#                  lnrre(dat$Control_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
#                        dat$Sample_size_control,  dat$Sample_size_opposite_sex)[[2]])

# here we create CM/F or CF/M
dat2$yi <- ifelse(effect_type == "longevity", 
                  lnrrm(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable,
                        dat$Sample_size_sterilization, dat$Sample_size_opposite_sex, 
                        cvs[["cv2_trt"]],cvs[["cv2_opst"]])[[1]], 
                  lnrrp(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
                        dat$Sample_size_sterilization, dat$Sample_size_opposite_sex)[[1]])

dat2$vi <- ifelse(effect_type == "longevity", 
                  lnrrm(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable,
                        dat$Sample_size_sterilization, dat$Sample_size_opposite_sex, 
                        cvs[["cv2_trt"]],cvs[["cv2_opst"]])[[2]], 
                  lnrrp(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
                        dat$Sample_size_sterilization, dat$Sample_size_opposite_sex)[[2]])

# 
# dat2$y2 <- ifelse(effect_type == "longevity", 
#                   lnrrm(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable,
#                         dat$Sample_size_sterilization, dat$Sample_size_opposite_sex, 
#                         cvs[["cv2_trt"]],cvs[["cv2_opst"]])[[1]], 
#                   lnrre(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
#                         dat$Sample_size_sterilization, dat$Sample_size_opposite_sex)[[1]])
# 
# dat2$v2 <- ifelse(effect_type == "longevity", 
#                   lnrrm(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable,
#                         dat$Sample_size_sterilization, dat$Sample_size_opposite_sex, 
#                         cvs[["cv2_trt"]],cvs[["cv2_opst"]])[[2]], 
#                   lnrre(dat$Treatment_lifespan_variable, dat$Opposite_sex_lifespan_variable, 
#                         dat$Sample_size_sterilization, dat$Sample_size_opposite_sex)[[2]])

# putting two data frames
dat_long <- rbind(dat1, dat2)

# putt 2 new column

dat_long$Obs <- factor(1:dim(dat_long)[[1]])
dat_long$Comp_type <- as.factor(rep(c("both_normal", "one_castrated"), each = dim(dat_long)[[1]]/2))

dat_long$Comp_type_Sex <- paste(dat_long$Comp_type, dat_long$Sex, sep = "_")

dat_long$yi <- ifelse(dat_long$Comp_type_Sex ==  "one_castrated_Female" | dat_long$Comp_type_Sex == "both_normal_Female", -1*dat_long$yi, dat_long$yi)

dat_long %>% filter(!is.na(yi), !is.na(vi)) -> dat_long

#dim(dat_long)

Rodent data

This data is a subset of the main data only including data from rodents. This is used to test the effect of sterilization/castration on life stage (for these species, we were able to get more fine scale data). This dataset has two more variables: 1) Age_at_treatment (age in days when treatment was applied) and 2) Day_to_maturity (age in days when animals become mature). As also mentioned below, the analyses associated with this data set is post-hoc (not planned before data collection).

#sdat <- read_csv(here("data", "data2_15022022.csv"), na = c("", "NA")) 
rdat <- read_csv(here("data",  "literature","data3_05052022.csv"), na = c("", "NA")) 

rdat <- rdat %>% filter(is.na(Treatment_lifespan_variable) == FALSE) %>% 
  #filter(Type_of_sterilization != "Vasectomy") %>% 
  mutate_if(is.character, as.factor) 

#dim(rdat)

# separating two kinds

effect_type_r <- ifelse(str_detect(rdat$Lifespan_parameter, "Me"), "longevity", "mortality")


# effect-level ID
#dat$Species_Latin <- gsub("Macaca Fascicularis", "Macaca fascicularis", dat$Species_Latin) #fix a typo in species name
rdat$Effect_ID <- 1:nrow(rdat)
rdat$Phylogeny <- sub(" ", "_",  rdat$Species_Latin)
rdat$Effect_type <- effect_type_r

# key variables
#names(rdat)
#unique(rdat$Species_Latin)

kable(rdat, "html") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", 
    height = "250px")

Order_extracted	Study	Controlled_treatments	Type_of_sterilization	Gonads_removed	Control_treatment	Sex	Day_to_matuarity	Species_Latin	Species	Strain	Environment	Wild_or_semi_wild	Age_at_treatment	Age_at_treatment_continuous	Maturity_at_treatment	Maturity_at_treatment_ordinal	Duration_of_treatment	Shared_control	Control_lifespan_variable	Treatment_lifespan_variable	Opposite_sex_lifespan_variable	Error_control	Error_experimental	Error_opposite_sex	Lifespan_parameter	Lifespan_unit	Error_unit	Error_control_SD	Error_experimental_SD	Error_opposite_sex_SD	Coefficent_difference_to_control	Lower_interval	Upper_interval	Coefficent_unit	Sample_size_control	Sample_size_sterilization	Sample_size_opposite_sex	Notes	Effect_ID	Phylogeny	Effect_type
13	Zakeri et al 2019	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	NMRI	Laboratory	No	10 months	304.0	Adult (old)	4	NA	11	0.360	0.500	NA	NA	NA	NA	Survival rate (%)	11.5 months	NA	NA	NA	NA	NA	NA	NA	NA	16	16	NA	Its the sterilization treatment that is compared to two different types of control in this study	1	Mus_musculus	mortality
14	Zakeri et al 2019	Yes	Ovariectomy	Yes	Sham surgery	Female	42	Mus musculus	Mice	NMRI	Laboratory	No	10 months	304.0	Adult (old)	4	NA	11	0.330	0.500	NA	NA	NA	NA	Survival rate (%)	11.5 months	NA	NA	NA	NA	NA	NA	NA	NA	16	16	NA	Its the sterilization treatment that is compared to two different types of control in this study	2	Mus_musculus	mortality
15	Dorner 1973	Yes	Castration	Yes	Untreated	Male	70	Rattus norvegicus	Rats	Sprague-Dawley-Stammes	Laboratory	No	Day after birth	2.0	Birth	1	NA	12	570.000	696.000	NA	122.00	132.00	NA	Mean	days	Standard Deviation	122.00000	132.00000	NA	NA	NA	NA	NA	12	8	NA	NA	3	Rattus_norvegicus	longevity
16	Asdell et al 1967	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	90	Rattus norvegicus	Rats	Cornell Nutrion colony	Laboratory	No	Between 38-42 days	40.0	Puberty	3	NA	13	742.000	669.000	615	24.00	26.00	21.00	Mean	days	S.E.M	169.70563	183.84776	148.49242	NA	NA	NA	NA	50	50	50	Also data for mated females but havent included as would be a different environment (e.g. With males)	4	Rattus_norvegicus	longevity
17	Asdell et al 1967	Yes	Castration	Yes	Intact (no surgery)	Male	70	Rattus norvegicus	Rats	Cornell Nutrion colony	Laboratory	No	Between 39-42 days	41.0	Puberty	3	NA	14	615.000	651.000	742	21.00	26.00	24.00	Mean	days	S.E.M	148.49242	183.84776	169.70563	NA	NA	NA	NA	50	50	50	NA	5	Rattus_norvegicus	longevity
18	Asdell and Joshi 1976	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	90	Rattus norvegicus	Rats	Manor-Wistar	Laboratory	No	45 days old	45.0	Puberty	3	NA	15	654.000	844.000	661	24.00	24.00	30.00	Mean	days	S.E.M	169.70563	169.70563	212.13203	NA	NA	NA	NA	50	50	50	NA	6	Rattus_norvegicus	longevity
19	Asdell and Joshi 1976	Yes	Castration	Yes	Intact (no surgery)	Male	70	Rattus norvegicus	Rats	Manor-Wistar	Laboratory	No	45 days old	45.0	Puberty	3	NA	16	661.000	775.000	654	30.00	30.00	24.00	Mean	days	S.E.M	212.13203	212.13203	169.70563	NA	NA	NA	NA	50	50	50	NA	7	Rattus_norvegicus	longevity
20	Arriola Apelo et al 2020	Yes	Castration	Yes	Sham surgery	Male	42	Mus musculus	Mice	C57BL6	Laboratory	No	21 days	21.0	Prepuberty	2	NA	17	1006.000	978.000	853	34.30	37.45	26.25	Median	days	S.E.M	145.52258	183.46678	136.39900	NA	NA	NA	NA	18	24	27	NA	8	Mus_musculus	longevity
21	Arriola Apelo et al 2020	Yes	Ovariectomy	Yes	Sham surgery	Female	42	Mus musculus	Mice	C57BL6	Laboratory	No	21 days	21.0	Prepuberty	2	NA	18	853.000	916.000	1006	26.25	49.36	34.30	Median	days	S.E.M	136.39900	231.51892	145.52258	NA	NA	NA	NA	27	22	18	NA	9	Mus_musculus	longevity
22	Benedusi et al 2015	Yes	Castration	Yes	Sham surgery	Male	42	Mus musculus	Mice	C57BL76 (ERE-LucRepTOP™)	Laboratory	No	5 Months	152.0	Adult (old)	4	NA	19	0.100	0.350	NA	NA	NA	NA	Survival rate (%)	15 Months	NA	NA	NA	NA	NA	NA	NA	NA	20	20	NA	NA	10	Mus_musculus	mortality
24	Cargil et al 2003	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	CBA	Laboratory	No	21 days	21.0	Prepuberty	2	NA	21	599.290	578.640	NA	30.45	35.60	NA	Median	Days	S.E.M	158.22284	178.00000	NA	NA	NA	NA	NA	27	25	NA	Extracted median lifespan from data in figure and calculated SD	11	Mus_musculus	longevity
31	Drori and Folman 1976	Yes	Castration	Yes	Intact (no surgery)	Male	42	Rattus norvegicus	Rats	Albino	Laboratory	No	38-44 days. Stated as prepuberty	41.0	Prepuberty	2	NA	27	727.000	817.000	849	26.00	32.00	26.00	Mean	Days	S.E.M	182.00000	224.00000	182.00000	NA	NA	NA	NA	49	49	49	Authors state that they castrated animals shortly before puberty, so coded as prepuberty	12	Rattus_norvegicus	longevity
32	Garratt et al 2021	Yes	Castration	Yes	Sham surgery	Male	70	Mus musculus	Mice	C57BL6	Laboratory	No	7-8 weeks	53.0	Adult (young)	4	NA	28	952.000	960.000	956	20.70	36.40	28.50	Median	Days	S.E.M	117.09688	218.40000	156.10093	NA	NA	NA	NA	32	36	30	NA	13	Mus_musculus	longevity
42	Holland et al 1977	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	RFM	Laboratory	No	3-4 weeks	25.0	Prepuberty	2	NA	34	638.000	628.000	NA	16.00	16.00	NA	Mean	Days	S.E.M	167.04490	162.38226	NA	NA	NA	NA	NA	109	103	NA	Just used data from non-irradiated group. Lots of pathology data	14	Mus_musculus	longevity
45	Sichuk 1965	Yes	Castration	Yes	Sham surgery	Male	48	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	No	6 weeks	42.0	Puberty	3	NA	37	612.000	578.000	589	NA	NA	NA	Mean	Days	NA	222.91000	151.30000	222.95000	NA	NA	NA	NA	92	90	94	Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group	15	Mesocricetus_auratus	longevity
46	Sichuk 1965	Yes	Ovariectomy	Yes	Sham surgery	Female	48	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	No	6 weeks	42.0	Puberty	3	NA	38	589.000	586.000	612	NA	NA	NA	Mean	Days	NA	222.95000	155.44000	222.91000	NA	NA	NA	NA	94	92	92	Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group	16	Mesocricetus_auratus	longevity
51	Slonaker 1930	Yes	Castration	Yes	Intact (no surgery)	Male	70	Rattus norvegicus	Rats	Albino	Laboratory	No	44 days. Testes had decended by the operation	44.0	Adult (young)	4	NA	43	788.000	770.000	863	22.25	28.00	27.69	Mean	Days	Probable error	32.98740	41.51223	41.05263	NA	NA	NA	NA	10	8	17	NA	17	Rattus_norvegicus	longevity
52	Slonaker 1930	Yes	Vasectomy	No	Intact (no surgery)	Male	70	Rattus norvegicus	Rats	Albino	Laboratory	No	46.5 days. Testes had decended by the operation	47.0	Adult (young)	4	NA	43	788.000	685.000	863	22.25	39.72	27.69	Mean	Days	Probable error	32.98740	58.88807	41.05263	NA	NA	NA	NA	10	12	17	Standard deviation calculated from probable error as P.E./0.6745. Calculation derived from P.E. Wikipedia page	18	Rattus_norvegicus	longevity
53	Slonaker 1930	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	90	Rattus norvegicus	Rats	Albino	Laboratory	No	27.5 days	28.0	Prepuberty	2	NA	44	863.000	755.000	788	27.69	22.15	22.25	Mean	Days	Probable error	41.05263	32.83914	32.98740	NA	NA	NA	NA	17	37	10	NA	19	Rattus_norvegicus	longevity
54	Slonaker 1930	Yes	Hysterectomy	No	Intact (no surgery)	Female	90	Rattus norvegicus	Rats	Albino	Laboratory	No	29 days	29.0	Prepuberty	2	NA	44	863.000	855.000	788	27.69	12.67	22.25	Mean	Days	Probable error	41.05263	18.78428	32.98740	NA	NA	NA	NA	17	60	10	NA	20	Rattus_norvegicus	longevity
55	Storer et al 1982	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	RFM	Laboratory	No	50 days	50.0	Adult (young)	4	NA	45	643.400	662.200	NA	5.91	7.31	NA	Mean	Days	S.E.M.	161.31161	134.19376	NA	NA	NA	NA	NA	745	337	NA	Non-irradiated controls from an irradiation experiment	21	Mus_musculus	longevity
56	Storer et al 1982	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	Balb/c	Laboratory	No	50 days	50.0	Adult (young)	4	NA	46	762.900	795.500	NA	6.21	10.95	NA	Mean	Days	S.E.M.	179.01611	197.70740	NA	NA	NA	NA	NA	831	326	NA	Non-irradiated controls from an irradiation experiment	22	Mus_musculus	longevity
61	Mason et al 2009	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	CBA	Laboratory	No	21 days	21.0	Prepuberty	2	NA	51	727.600	715.000	NA	15.90	20.00	NA	Mean	Days	S.E.M	89.94398	101.98039	NA	NA	NA	NA	NA	32	26	NA	Worked out sample size from fig 4 This and the other entry for this paper have two different control comparisons	23	Mus_musculus	longevity
62	Mason et al 2009	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	CBA	Laboratory	No	21 days	21.0	Prepuberty	2	NA	51	725.600	715.000	NA	20.40	20.00	NA	Mean	Days	S.E.M	117.18908	101.98039	NA	NA	NA	NA	NA	33	26	NA	Worked out sample size from fig 4	24	Mus_musculus	longevity
63	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Male	70	Rattus norvegicus	Rats	Lewis	Laboratory	No	Birth	1.0	Birth	1	NA	52	454.000	521.000	484	18.00	27.00	19.00	Mean	Days	S.E.M	108.00000	174.98000	123.13407	NA	NA	NA	NA	36	42	42	NA	25	Rattus_norvegicus	longevity
64	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Male	70	Rattus norvegicus	Rats	Lewis	Laboratory	No	22-28 days	25.0	Prepuberty	2	NA	52	454.000	488.000	484	18.00	28.00	19.00	Mean	Days	S.E.M	108.00000	165.65023	123.13407	NA	NA	NA	NA	36	35	42	NA	26	Rattus_norvegicus	longevity
65	Talbert and Hamilton 1965	Yes	Castration	Yes	Sham surgery	Male	70	Rattus norvegicus	Rats	Lewis	Laboratory	No	100 days	100.0	Adult (young)	4	NA	52	454.000	439.000	484	18.00	25.00	19.00	Mean	Days	S.E.M	108.00000	119.89579	123.13407	NA	NA	NA	NA	36	23	42	NA	27	Rattus_norvegicus	longevity
66	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Lewis	Laboratory	No	Birth	1.0	Birth	1	NA	53	484.000	574.000	454	19.00	33.00	18.00	Mean	Days	S.E.M	123.13407	183.73622	108.00000	NA	NA	NA	NA	42	31	36	NA	28	Rattus_norvegicus	longevity
67	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Lewis	Laboratory	No	22-28 days	25.0	Prepuberty	2	NA	53	484.000	480.000	454	19.00	44.00	18.00	Mean	Days	S.E.M	123.13407	206.37829	108.00000	NA	NA	NA	NA	42	22	36	NA	29	Rattus_norvegicus	longevity
68	Talbert and Hamilton 1965	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Lewis	Laboratory	No	100 days	100.0	Adult (young)	4	NA	53	484.000	515.000	454	19.00	41.00	18.00	Mean	Days	S.E.M	123.13407	183.35757	108.00000	NA	NA	NA	NA	42	20	36	NA	30	Rattus_norvegicus	longevity
94	Muhlock 1959	Yes	Castration	Yes	Intact (no surgery)	Male	42	Mus musculus	Mice	DBA	Laboratory	No	Weaning (1month)	30.0	Prepuberty	2	NA	75	578.000	595.000	667	10.12	11.44	9.57	Mean	days	S.E.M	78.38918	102.32247	88.74853	NA	NA	NA	NA	60	80	86	Extracted data and calculated mean and SE from graph	31	Mus_musculus	longevity
95	Muhlock 1959	Yes	Ovariectomy	Yes	Intact (no surgery)	Female	42	Mus musculus	Mice	DBA	Laboratory	No	Weaning (1 month)	30.0	Prepuberty	2	NA	76	667.000	627.000	578	9.57	10.67	10.12	Mean	days	S.E.M	88.74853	87.98707	78.38918	NA	NA	NA	NA	86	68	60	Extracted data and calculated mean and SE from graph	32	Mus_musculus	longevity
99	Iwasa et al 2018	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	23 weeks	161.0	Late adult	4	NA	80	0.430	0.860	NA	NA	NA	NA	Survival rate (%)	~85 weeks.	NA	NA	NA	NA	NA	NA	NA	NA	8	7	NA	Calculated from a partial survival curve. Looked at when 50% of the control group died and then the number alive in the treatment group at this point	33	Rattus_norvegicus	mortality
114	Cheng 2019	Yes	Castration	Yes	Sham surgery	Male	42	Mus musculus	Mice	UMHet3	Laboratory	No	Under 30 days	17.5	Prepuberty	2	NA	91	0.810	0.970	NA	NA	NA	NA	Survival rate (%)	500 days	NA	NA	NA	NA	NA	NA	NA	NA	238	238	NA	NA	34	Mus_musculus	mortality
132	Sato et al 1997	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	9 months	274.0	Adult	4	NA	109	0.800	0.971	NA	NA	NA	NA	Survival rate (%)	6 months	NA	NA	NA	NA	NA	NA	NA	NA	35	35	NA	NA	35	Rattus_norvegicus	mortality
135	Pullinger and Head 1964	Yes	Ovariectomy	Yes	untreated	Female	42	Mus musculus	Mice	C3Hf	Laboratory	No	56-111 days of age	84.0	Adult	4	NA	112	0.370	0.230	NA	NA	NA	NA	Survival rate (%)	to 30 months	NA	NA	NA	NA	NA	NA	NA	NA	114	40	NA	"average lifespan" in months is also provided but no error. Calculated the percentage surviving to 30 months as dead ranges are provided in 6 brackets and this is the closest to the median for control females. This group is control virgin females (OVX data was shared and compared to breeding females in the other female comparison for this paper but I removed it because I dont think social environment was comparable)	36	Mus_musculus	mortality
145	Hotchkiss 1995	Yes	Ovariectomy	Yes	Sham surgery	Female	90	Rattus norvegicus	Rats	Sprague-Dawley	Laboratory	No	90 days	90.0	Adult	4	NA	120	0.333	0.833	NA	NA	NA	NA	Survival rate (%)	to 630 days	NA	NA	NA	NA	NA	NA	NA	NA	12	12	NA	Survival to 630 days. Animals that presented with subquaneous tumors had these surgically removed.	37	Rattus_norvegicus	mortality
151	Phelan 1995	Yes	Ovariectomy	Yes	Sham surgery	Female	42	Mus musculus	Mice	Swiss	Laboratory	No	Weaning	25.0	Weaning	2	NA	125	0.410	0.620	NA	NA	NA	NA	Survival rate (%)	To 800 days	NA	NA	NA	NA	NA	NA	NA	NA	30	30	NA	Animals were maintained on a 90% of adlibitum diet. This was a control group for a sepeate CR study and they state stopped the animals getting fat.	38	Mus_musculus	mortality
156	Wang et al 2021	Yes	Castration	Yes	Sham surgery	Male	42	Mus musculus	Mice	C57BL6	NA	No	8 months	243.0	Adult	4	NA	130	933.500	938.500	NA	NA	NA	NA	Median	NA	SD	129.00000	117.00000	NA	NA	NA	NA	NA	22	22	NA	Extended figure 3I	39	Mus_musculus	longevity
157	Wang et al 2021	Yes	Castration	Yes	Sham surgery	Male	42	Mus musculus	Mice	NA	NA	No	18 months	548.0	Adult	4	NA	131	974.000	959.000	NA	NA	NA	NA	Median	NA	SD	95.00000	93.00000	NA	NA	NA	NA	NA	19	19	NA	Fig 6q Animals had been injected with a control shRNA	40	Mus_musculus	longevity

Calculating effect size for the rodent data

# let's get CVs

rdat %>% group_by(Study) %>% summarise(cv2_cont = mean((Error_control_SD/Control_lifespan_variable)^2, na.rm = T), cv2_trt = mean((Error_experimental_SD/Treatment_lifespan_variable)^2, na.rm = T), cv2_opst = mean((Error_opposite_sex_SD/Opposite_sex_lifespan_variable)^2, na.rm = T), n_cont = mean(Sample_size_control, na.rm = T), n_trt =  mean(Sample_size_sterilization, na.rm = T), n_opst =  mean(Sample_size_opposite_sex, na.rm = T)) %>% 
  ungroup() %>% 
  summarise(cv2_cont = weighted.mean(cv2_cont, n_cont, na.rm = T), cv2_trt = weighted.mean(cv2_trt, n_trt, na.rm = T), cv2_opst = weighted.mean(cv2_opst, n_opst, na.rm = T)) -> cvs

# lnRR
# using CV
rdat$yi <- ifelse(effect_type_r == "longevity", lnrrm(rdat$Treatment_lifespan_variable, rdat$Control_lifespan_variable, rdat$Sample_size_sterilization, rdat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[1]], lnrrp(rdat$Treatment_lifespan_variable, rdat$Control_lifespan_variable, rdat$Sample_size_sterilization, rdat$Sample_size_control)[[1]])

rdat$vi <- ifelse(effect_type_r == "longevity", lnrrm(rdat$Treatment_lifespan_variable, rdat$Control_lifespan_variable, rdat$Sample_size_sterilization, rdat$Sample_size_control, cvs[["cv2_trt"]],cvs[["cv2_cont"]])[[2]], lnrrp(rdat$Treatment_lifespan_variable, rdat$Control_lifespan_variable, rdat$Sample_size_sterilization, rdat$Sample_size_control)[[2]])

#str(rdat)

This dataset is a part of the full data and only has data from rodent species.

All-combination data

This data sets is a subset of the main data and this includes only study which has all 4 groups: 1) control females, 2) control males, 3) treated females and 4) treated males.

#sdat <- read_csv(here("data", "data2_15022022.csv"), na = c("", "NA")) 
sdat <- read_csv(here("data", "literature", "data2_19042022.csv"), na = c("", "NA")) 
effect_type_s <- ifelse(str_detect(sdat$Lifespan_parameter, "Me"), "longevity", "mortality")


# effect-level ID

sdat$Effect_ID <- 1:nrow(sdat)
sdat$Phylogeny <- sub(" ", "_",  sdat$Species_Latin)
sdat$Effect_type <- effect_type_s

kable(sdat, "html") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", 
    height = "250px")

Order_extracted	Study	Controlled_treatments	Wild_or_semi_wild	Type_of_sterilization	Gonads_removed	Control_treatment	Species_Latin	Species	Strain	Environment	Age_at_treatment	Shared_control	Male_control_lifespan_variable	Error_male_control_SD	Sample_size_male_control	Male_sterilization_lifespan_variable	Error_male_sterilization_SD	Sample_size_male_sterilization	Female_control_lifespan_variable	Error_female_control_SD	Sample_size_female_control	Female_sterilization_lifespan_variable	Error_female_sterilization_SD	Sample_size_female_sterilization	Lifespan_parameter	Lifespan_unit	Notes	Effect_ID	Phylogeny	Effect_type
20	Arriola Apelo et al 2020	Yes	No	Castration	Yes	Sham surgery	Mus musculus	Mice	C57BL6	Laboratory	21 days	17	1006.00	145.522576	18	978.00	171.617460	24	853.00	136.399001	27	916.00	203.516494	22	Median	days	NA	1	Mus_musculus	longevity
19	Asdell and Joshi 1976	Yes	No	Castration	Yes	Intact (no surgery)	Rattus norvegicus	Rat	Manor-Wistar	Laboratory	45 days old	16	661.00	212.132034	50	775.00	212.132034	50	654.00	169.705627	50	844.00	169.705627	50	Mean	days	NA	2	Rattus_norvegicus	longevity
17	Asdell et al 1967	Yes	No	Castration	Yes	Intact (no surgery)	Rattus norvegicus	Rat	Cornell Nutrion colony	Laboratory	Between 38-42 days	14	615.00	148.492424	50	651.00	183.847763	50	742.00	169.705627	50	669.00	183.847763	50	Mean	days	NA	3	Rattus_norvegicus	longevity
115	Bronson 1981	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Various breeds	Domestic	Various	92	4.97	3.660000	219	7.34	4.290000	265	6.65	5.660000	99	9.11	5.130000	220	Mean	Years - for cats surviving to two years old	Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.	4	Felis_catus	longevity
117	Bronson 1982	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	94	8.00	5.200000	755	9.90	6.800000	54	7.70	4.400000	224	8.80	6.900000	528	Mean (from those alive after 2)	Years	Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison	5	Canis_lupus	longevity
72	Hamilton 1965	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Various breeds	Domestic	Before 1 year	57	3.20	2.741168	65	6.80	4.492661	60	7.70	5.178726	58	9.20	4.656522	28	Mean	Years	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	6	Felis_catus	longevity
74	Hamilton 1965	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Various breeds	Domestic	Before 1 year	59	6.10	4.713343	51	8.50	4.913980	77	7.40	5.091169	50	8.40	4.762825	45	Mean	Years	Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.	7	Felis_catus	longevity
33	Hamilton et al 1969	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Outbred	Domestic	Various	29	5.30	4.136520	97	8.10	4.820332	201	7.70	4.794163	85	8.20	4.503332	75	Mean	Years	Pooled data for all ages which is consistent for both castrated and spayed of both sexes.	8	Felis_catus	longevity
38	Hamilton et al 1969	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Name breeds	Domestic	Various, median 6 months	32	4.60	3.500000	25	6.90	4.971428	71	6.20	5.040000	36	8.20	4.723071	34	Mean	Years	NA	9	Felis_catus	longevity
57	Hoffman et al 2018	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	47	10.86	3.327386	915	11.64	2.033618	844	10.86	3.685485	693	12.12	5.766116	921	Mean	Years	Vetcompass database	10	Canis_lupus	longevity
59	Hoffman et al 2018	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	49	8.00	8.556337	14941	9.21	4.241509	11244	7.68	6.018372	7392	9.73	5.599714	19598	Mean	Years	VMDB - individual data for breeds available in supplementary, but just mean lifespan without error	11	Canis_lupus	longevity
76	Huang et al 2017	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	61	9.00	5.941000	839	12.00	3.723000	332	10.00	5.947000	528	12.00	3.938000	607	Median	Years	Interquartile range Intact, 5.0-13.0; castrated 9.0-14.0	12	Canis_lupus	longevity
43	Kirkman and Yau	No	No	Castration	Yes	Intact (no surgery)	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	Unknown - not given	35	632.00	222.910000	629	508.00	151.300000	72	543.00	222.950000	578	391.00	155.440000	31	Mean	Days	Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 550-850 for intact males, 350-550 for castrated males)	13	Mesocricetus_auratus	longevity
47	Mitchel et al 1999	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	39	131.00	50.029192	1277	128.00	46.058550	291	130.00	51.951131	833	144.00	40.249224	720	Mean	Months	Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death	14	Canis_lupus	longevity
94	Muhlock 1959	Yes	No	Castration	Yes	Intact (no surgery)	Mus musculus	Mouse	DBA	Laboratory	Weaning	75	578.00	78.389183	60	595.00	102.322471	80	667.00	88.748529	86	627.00	87.987074	68	Mean	days	Extracted data and calculated mean and SE from graph	15	Mus_musculus	longevity
105	Oneil et al 2013	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	83	11.99	NA	1464	11.99	NA	1224	11.59	NA	1082	12.39	NA	1304	Mean	Years	Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated	16	Canis_lupus	longevity
107	Oneil et al 2015	No	No	Castration	Yes	Intact (no surgery)	Felis catus	Cats	Various breeds	Domestic	Various	85	12.81	NA	704	14.71	NA	1296	14.61	NA	707	15.21	NA	1302	Mean	Years	Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated	17	Felis_catus	longevity
30	Reedy et al 2016	Yes	Yes	Castration	Yes	Sham surgery	Anolis sagrei	Anole lizards	NA	Wild	Unknown - wild caught	26	0.55	NA	60	0.28	NA	60	0.25	NA	110	0.21	NA	110	Survival rate (%)	10 weeks (of breeding season)	NA	18	Anolis_sagrei	mortality
45	Sichuk 1965	Yes	No	Castration	Yes	Sham surgery	Mesocricetus auratus	Hampsters	Syrian Hampsters	Laboratory	6 weeks	37	612.00	222.910000	92	578.00	151.300000	90	589.00	222.950000	94	586.00	155.440000	92	Mean	Days	Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group	19	Mesocricetus_auratus	longevity
51	Slonaker 1929	Yes	No	Castration	Yes	Intact (no surgery)	Rattus norvegicus	Rat	Albino	Laboratory	44 days	43	788.00	32.987398	10	770.00	41.512231	8	863.00	41.052632	17	855.00	18.784285	60	Mean	Days	Hyesterectomy female comparison	20	Rattus_norvegicus	longevity
51	Slonaker 1929	Yes	No	Castration	Yes	Intact (no surgery)	Rattus norvegicus	Rat	Albino	Laboratory	44 days	43	788.00	32.987398	10	770.00	41.512231	8	863.00	41.052632	17	755.00	32.839140	37	Mean	Days	Ovariectomy female comparison	21	Rattus_norvegicus	longevity
63	Talbert and Hamilton 1965	Yes	No	Castration	Yes	Sham surgery	Rattus norvegicus	Rats	Lewis	Laboratory	Birth	52	454.00	108.000000	36	521.00	174.979999	42	484.00	123.134073	42	574.00	183.736224	31	Mean	Days	NA	22	Rattus_norvegicus	longevity
64	Talbert and Hamilton 1965	Yes	No	Castration	Yes	Sham surgery	Rattus norvegicus	Rats	Lewis	Laboratory	22-28 days	52	454.00	108.000000	36	488.00	165.650234	35	484.00	123.134073	42	480.00	206.378293	22	Mean	Days	NA	23	Rattus_norvegicus	longevity
65	Talbert and Hamilton 1965	Yes	No	Castration	Yes	Sham surgery	Rattus norvegicus	Rats	Lewis	Laboratory	100 days	52	454.00	108.000000	36	439.00	119.895788	23	484.00	123.134073	42	515.00	183.357574	20	Mean	Days	NA	24	Rattus_norvegicus	longevity
87	Urfer et al 2019	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Various breeds	Domestic	Various	68	14.09	18.753700	322958	14.15	12.400487	909894	13.77	22.107487	230974	14.35	9.710121	906252	Median	years	95% confidence intervals Intact Male: 14.03-14.16; Castrated male: 14.13-14.18	25	Canis_lupus	longevity
159	Tidiere 2016	No	No	Unknown	Unknown	Intact (no surgery)	Varecia rubra	Red ruffed lemur	NA	Zoo	Unknown	133	16.18	13.436925	927	19.40	11.495374	38	15.65	12.454792	689	19.84	10.210810	67	Mean	Years	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	26	Varecia_rubra	longevity
161	Tidiere 2016	No	No	Unknown	Unknown	Intact (no surgery)	Varecia variegata	Black and white ruffed lemur	NA	Zoo	Unknown	134	13.82	14.485185	1999	15.86	12.320698	37	13.95	13.122827	1542	18.03	12.489796	36	Mean	Years	Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species	27	Varecia_variegata	longevity
129	Kent et al 2018	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	Golden retriver	Domestic	Unknown	106	8.68	3.230000	118	9.35	2.710000	228	5.89	3.270000	58	9.51	2.740000	248	Median	Years	Standard deviation calculated from the range according to Wan et al 2014	28	Canis_lupus	longevity
136	Robertson et al 1961	No	No	Castration	Yes	untreated	Oncorhynchus nerka	Kokanee salmon	NA	Experimental pond	2 years 1 month	113	4.05	0.590000	41	5.31	1.600000	13	4.26	0.550000	58	5.89	1.630000	16	Mean	Years	Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.	29	Oncorhynchus_nerka	longevity
141	Urfer et al 2020	No	No	Castration	Yes	Intact (no surgery)	Canis lupus	Dogs	NA	Various	Various	117	15.00	17.598000	2115	15.20	16.530000	8567	14.10	20.090000	1551	15.80	16.670000	8711	Median survival time	Years	Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included	30	Canis_lupus	longevity
162	Larsen1969	Yes	No	Gonadectomy	Yes	Intact (no surgery)	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	Prior to sexual maturity - Jan	135	0.10	NA	10	0.27	NA	10	0.10	NA	10	0.30	NA	10	Survival rate (%)	May (after spawning)	Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract	31	Lamperta_fluviatilis	mortality
164	Larsen 1973	Yes	No	Gonadectomy	Yes	Intact (no surgery)	Lamperta fluviatilis	River Lamprey	NA	Lab but wild caught	Prior to sexual maturity - Either Jan or October prior year	137	0.02	NA	50	0.25	NA	20	0.06	NA	17	0.25	NA	20	Survival rate (%)	May (after spawning)	Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 11	32	Lamperta_fluviatilis	mortality

Calculating effect size for the matching data

# we create a longer data format

sdat1 <- sdat
sdat2 <- sdat
# lnRR

# here we create the ratio of M/F
sdat1$yi <- ifelse(effect_type_s == "longevity", 
                  lnrrm(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable,
                        sdat$Sample_size_male_control, sdat$Sample_size_female_control, 
                        cvs[["cv2_cont"]],cvs[["cv2_cont"]])[[1]], 
                  lnrrp(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, 
                        sdat$Sample_size_male_control,  sdat$Sample_size_female_control)[[1]])

sdat1$vi <- ifelse(effect_type_s == "longevity", 
                  lnrrm(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable,
                        sdat$Sample_size_male_control, sdat$Sample_size_female_control, 
                        cvs[["cv2_cont"]],cvs[["cv2_cont"]])[[2]], 
                  lnrrp(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, 
                        sdat$Sample_size_male_control,  sdat$Sample_size_female_control)[[2]])

# here we create CM/CF
sdat2$yi <- ifelse(effect_type_s == "longevity", 
                  lnrrm(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable,
                        sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization, 
                        cvs[["cv2_trt"]],cvs[["cv2_trt"]])[[1]], 
                  lnrrp(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, 
                        sdat$Sample_size_male_sterilization,  sdat$Sample_size_female_sterilization)[[1]])

sdat2$vi <-  ifelse(effect_type_s == "longevity", 
                   lnrrm(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable,
                         sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization, 
                         cvs[["cv2_trt"]],cvs[["cv2_trt"]])[[2]], 
                   lnrrp(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, 
                         sdat$Sample_size_male_sterilization,  sdat$Sample_size_female_sterilization)[[2]])

# merging sdata frames
sdat_long <- rbind(sdat1, sdat2)

# putt 2 new column

sdat_long$Obs <- factor(1:dim(sdat_long)[[1]])
sdat_long$Comp_type <- as.factor(rep(c("both_normal", "both_castrated"), each = dim(sdat_long)[[1]]/2))

Meta-data

#sdat <- read_csv(here("data", "data2_15022022.csv"), na = c("", "NA")) 
mdat <- read_csv(here("data", "literature", "data_meta-data.csv")) 


kable(mdat, "html") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", 
    height = "250px")

Order_extracted	Serial number of the order of effect size extration
Study	Study data was taken from
Controlled_treatments	Whether treatment allocation and environments were derived in a controlled way
Type_of_sterilization	Type of sterilization used
Gonads_removed	Whether gonads were removed as part of the sterilization
Control_treatment	Type of treatment/sham manipulation applied to controls
Shamtreatment_moderator	Whether control groups involved sham treatments or not
Sex	Sex in which the effect of sterilization was assessed
Species_Latin	Latin name of species
Species	Common name
Strain	Strain of animal used where relevant
Environment	Type of environment that effects were assessed in
Wild_or_semi_wild	Whether the environment was wild/semi-wild or not
Age_at_treatment	Information on age at which sterilization was conducted
Maturity_at_treatment	When sterilization was conducted in relation to life stage
Maturity_at_treatment_ordinal	When sterilization was conducted as an ordinal variable (1=birth; 2 = pre-puberty, 3 = puberty, 4 = adulthood)
Duration_of_treatment	Duration of treatment if not permanent
Shared_control	Shared numbers denote that a shared control or treatment group was used in each of these comparisons
Control_lifespan_variable	Lifespan/survival variable for the control group
Treatment_lifespan_variable	Lifespan/survival variable for the sterilized group
Opposite_sex_lifespan_variable	Lifespan/survival variable for the opposite sex group if available
Error_control	Error value for control group as extracted from the original source
Error_experimental	Error value for sterilized group as extracted from the original source
Error_opposite_sex	Error value for opposite sex group as extracted from the original source
Lifespan_parameter	Type of survival data
Lifespan_unit	Unit of measurement for survival data
Error_unit	Unit of measurement for error in original source
Error_control_SD	Standard deviation of control group
Error_experimental_SD	Standard deviation of sterilized group
Error_opposite_sex_SD	Standard deviation of opposite sex group
Coefficent_difference_to_control	Coefficient estimate of difference to control group in lifespan from original source
Lower_interval	Lower confidence interval for coefficient estimate
Upper_interval	Upper confidence interval for coefficient estimate
Coefficent_unit	Unit of measurement for coefficient estimate
Sample_size_control	Sample size of control group
Sample_size_sterilization	Sample size of sterilization group
Sample_size_opposite_sex	Sample size of opposite sex group
Notes	Notes on study and data extraction

4 Summarise the dataset

Visualising missing data

Key predictors (moderators)

Visualise missing data in 6 key variables (moderators): - Sex, - Wild_or_semi_wild, - Maturity_at_treatment_ordinal, - Gonads_removed, - Controlled_treatments, - Effect_type

Visualize pairwise associations between 6 key variables:

Alluvial diagrams for key predictor variables (moderators) - two groups of 3 moderators, grouped by similarity:

For Sex, Gonads_removed, & Maturity_at_treatment_ordinal

0 in Mautraity class is NA

#use ggalluvial (https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html)

# create a frequency table for first 3 moderator variables
# freq_1 <- as.data.frame(table(dat$Sex, dat$Gonads_removed, dat$Maturity_at_treatment_ordinal)) %>% rename(Sex = Var1, Gonads_removed = Var2, Maturity_at_treatment_ordinal = Var3)
# is_alluvia_form(as.data.frame(freq_1), axes = 1:3, silent = TRUE)
# #freq_1 %>% filter(Freq != 0) %>% arrange(desc(Freq)) #collapesd table of values, without 0s

 
# ggplot(data = freq_1,
#   aes(axis1 = Sex, axis2 = Gonads_removed, axis3 = Maturity_at_treatment_ordinal, y = Freq)) +
#   geom_alluvium(aes(fill = Sex)) +
#   geom_stratum(aes(fill = Sex))+
#   geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
# #theme_minimal() +
#   theme_void() +
#   theme(legend.position = "none",
#         plot.title = element_text(hjust = 0.5, vjust = 3),
#         axis.title.x = element_text(),
#         axis.text.x = element_text(face="bold")) +
#   scale_x_discrete(limits = c("Sex", "Gonads removed", "Maturity class"), expand = c(0.15, 0.05), position = "top") +
#   scale_fill_brewer(palette = "Set3") +
#   ggtitle("A. Subjects sex, manipulation type and maturity class")

#as above but with, but with Gonads_removed as first column, and different colours
# ggplot(data = freq_1,
#   aes(axis1 = Gonads_removed, axis2 = Sex, axis3 = Maturity_at_treatment_ordinal, y = Freq)) +
#   geom_alluvium(aes(fill = Gonads_removed)) +
#   geom_stratum(aes(fill = Gonads_removed))+
#   geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
#   theme_minimal() +
# #  theme_void() +
#   theme(legend.position = "none",
#         plot.title = element_text(hjust = 0.5, vjust = 3),
#         axis.title.x = element_text(),
#         axis.text.x = element_text(face="bold")) +
#   scale_x_discrete(limits = c("Gonads_removed", "Sex", "Maturity class"), expand = c(0.15, 0.05), position = "top") +
#   scale_fill_brewer(palette = "Pastel2") +
#   ggtitle("A. Subjects sex, manipulation and maturity ")

#NOTE: all rows with NA in Maturity class are removed from the plot 
#recode NA as 0
dat$Maturity_at_treatment_ordinal2 <- dat$Maturity_at_treatment_ordinal
dat$Maturity_at_treatment_ordinal2[is.na(dat$Maturity_at_treatment_ordinal2)] <- 0
# create a frequency table for first 3 moderator variables
freq_1 <- as.data.frame(table(dat$Sex, dat$Gonads_removed, dat$Maturity_at_treatment_ordinal2)) %>% rename(Sex = Var1, Gonads_removed = Var2, Maturity_at_treatment_ordinal = Var3)
is_alluvia_form(as.data.frame(freq_1), axes = 1:3, silent = TRUE)
#freq_1 %>% filter(Freq != 0) %>% arrange(desc(Freq)) #collapesd table of values, without 0s

 
# ggplot(data = freq_1,
#   aes(axis1 = Sex, axis2 = Gonads_removed, axis3 = Maturity_at_treatment_ordinal, y = Freq)) +
#   geom_alluvium(aes(fill = Sex)) +
#   geom_stratum(aes(fill = Sex))+
#   geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
# #theme_minimal() +
#   theme_void() +
#   theme(legend.position = "none",
#         plot.title = element_text(hjust = 0.5, vjust = 3),
#         axis.title.x = element_text(),
#         axis.text.x = element_text(face="bold")) +
#   scale_x_discrete(limits = c("Sex", "Gonads removed", "Maturity class"), expand = c(0.15, 0.05), position = "top") +
#   scale_fill_brewer(palette = "Set3") +
#   ggtitle("A. Subjects sex, manipulation type and maturity class")

#as above but with, but with Gonads_removed as first column, and different colours
ggplot(data = freq_1,
  aes(axis1 = Gonads_removed, axis2 = Sex, axis3 = Maturity_at_treatment_ordinal, y = Freq)) +
  geom_alluvium(aes(fill = Gonads_removed)) +
  geom_stratum(aes(fill = Gonads_removed))+
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  theme_minimal() +
#  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold")) +
  scale_x_discrete(limits = c("Gonads_removed", "Sex", "Maturity class"), expand = c(0.15, 0.05), position = "top") +
  scale_fill_brewer(palette = "Pastel2") +
  ggtitle("A. Subjects sex, manipulation and maturity ")

For Wild_or_semi_wild, Controlled_treatments, & Effect_type (Outcome type)

# create a frequency table for next 3 moderator variables
freq_2 <- as.data.frame(table(dat$Wild_or_semi_wild, dat$Controlled_treatments, dat$Effect_type)) %>% rename(Wild_or_semi_wild = Var1, Controlled_treatments = Var2, Effect_type = Var3)
is_alluvia_form(as.data.frame(freq_2), axes = 1:3, silent = TRUE)
#freq_2 %>% filter(Freq != 0) %>% arrange(desc(Freq)) #collapesd table of values, without 0s

ggplot(data = freq_2,
  aes(axis1 = Wild_or_semi_wild, axis2 = Controlled_treatments, axis3 = Effect_type, y = Freq)) +
  geom_alluvium(aes(fill = Wild_or_semi_wild)) +
  geom_stratum(aes(fill = Wild_or_semi_wild))+
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  theme_minimal() +
#  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold")) +
  scale_x_discrete(limits = c("Wild_or_semi_wild", "Controlled_treatments", "Outcome type"), expand = c(0.15, 0.05), position = "top") +
  scale_fill_brewer(palette = "Pastel1") +
  ggtitle("B. Experimental settings and outcome type")

#names(dat_key)

For Gonads_removed, Wild_or_semi_wild, Controlled_treatments, & Effect_type (Outcome type)

# create a frequency table for next 3 moderator variables
freq_3 <- as.data.frame(table(dat$Gonads_removed, dat$Wild_or_semi_wild, dat$Effect_type)) %>% rename(Gonads_removed = Var1, Wild_or_semi_wild = Var2, Effect_type = Var3)
is_alluvia_form(as.data.frame(freq_3), axes = 1:3, silent = TRUE)
#freq_2 %>% filter(Freq != 0) %>% arrange(desc(Freq)) #collapesd table of values, without 0s

ggplot(data = freq_3,
  aes(axis1 = Gonads_removed, axis2 = Wild_or_semi_wild, axis3 = Effect_type, y = Freq)) +
  geom_alluvium(aes(fill = Gonads_removed)) +
  geom_stratum(aes(fill = Gonads_removed))+
  geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
  theme_minimal() +
#  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, vjust = 3),
        axis.title.x = element_text(),
        axis.text.x = element_text(face="bold")) +
  scale_x_discrete(limits = c("Gonads_removed", "Wild_or_semi_wild", "Outcome type"), expand = c(0.15, 0.05), position = "top") +
  scale_fill_brewer(palette = "Set3") +
  ggtitle("C. Manipulation, subject and outcome types")

#names(dat_key)

This just shows a different combination of predictor variables (moderators) from plots A and B.

5 Building phylogenetic tree

Preparation

#names(dat)
myspecies <- as.character(unique(dat$Species_Latin)) #get list of species
#str_sort(myspecies) #visual check
#length(myspecies) #23 species
#length(unique(myspecies)) #23 unique species names

Using rotl package to retrieve synthetic species tree from Open Tree of Life: Rotl is an R package (https://peerj.com/preprints/1471/) allowing access to synthetic phylogenetic tree available at Open Tree of Life database (https://opentreeoflife.org/).

taxa <- tnrs_match_names(names = myspecies)
dim(taxa) #40 specias - all matched
table(taxa$approximate_match) #1 approximate match
taxa[taxa$approximate_match == TRUE, ] ##lamperta fluviatilis (search_string) will be presented as Perca fluviatilis (uniquw_name)

Get the initial tree

tree <- tol_induced_subtree(ott_ids = taxa[["ott_id"]], label_format = "name")  
# plot(tree, cex=.6, label.offset =.1, no.margin = TRUE) visual check

Check matching species & labels

#check overlap and differences with the taxa list
intersect(gsub("_"," ", tree$tip.label), myspecies) #22
setdiff(gsub("_"," ", tree$tip.label), myspecies) # "Perca fluviatilis"  
setdiff(myspecies, gsub("_"," ", tree$tip.label)) # "Lamperta fluviatilis"
tree$tip.label <- gsub("Perca_fluviatilis", "Lamperta_fluviatilis", tree$tip.label) #replace with the original name

#tree <- drop.tip(tree, "Equus_caballus")
#re-check overlap and differences with myspecies list
#intersect(myspecies, tree2$tip.label) #23
#setdiff(myspecies, tree2$tip.label) #0
#setdiff(tree2$tip.label, myspecies) #0

#check if the tree is really binary 
is.binary.tree(tree) #TRUE
# tree_binary$node.label <- NULL #you can delete internal node labels
# *NOTE:* no branch lengths are included, they can be created later via simulations.  

write.tree(tree, file=here("data", "tree_rotl.tre")) #save the tree

# *NOTE:* underscores within species names on tree tip labals are added automatically
# tree <- read.tree(file="plot_cooked_fish_MA.tre") #if you need to read in the tree
# tree$tip.label <- gsub("_"," ", tree$tip.label) #get rid of the underscores
# tree$node.label <- NULL #you can delete internal node labels

Plot phylogenetic tree

tree <- read.tree(here("data", "literature","tree_rotl.tre"))

plot(tree, cex=.6, label.offset =.1, no.margin = TRUE)

# #or plot to pdf
# pdf(here("figs/rotl_tree.pdf"), width=8, heigh=10)
# plot(tree, cex=0.6, label.offset =.1, no.margin = TRUE)
# dev.off()

6 Meta-analysis: main

Main model

# VCV matrix to model shared control 
V_matrix <- impute_covariance_matrix(vi = dat$vi, cluster = dat$Shared_control, r = 0.5)

# phylogeny to model
#tree <- read.tree(here("data/tree_rotl.tre"))
tree <- compute.brlen(tree)

cor_tree <- vcv(tree, corr = TRUE)

# checking the match
#match(unique(dat$Phylogeny), colnames(cor_tree))


# meta-analysis basics
# phylogenetic model
mod <-  rma.mv(yi, V = V_matrix, mod = ~ 1, 
               random = list(~1|Phylogeny, 
                             ~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               R = list(Phylogeny = cor_tree), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(mod) 

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.5192  -33.0384  -23.0384   -7.7254  -22.6436   

Variance Components:

            estim    sqrt  nlvls  fixed         factor    R 
sigma^2.1  0.0011  0.0325     22     no      Phylogeny  yes 
sigma^2.2  0.0166  0.1289     22     no  Species_Latin   no 
sigma^2.3  0.0133  0.1152     71     no          Study   no 
sigma^2.4  0.0080  0.0894    159     no      Effect_ID   no 

Test for Heterogeneity:
Q(df = 158) = 1366.5635, p-val < .0001

Model Results:

estimate      se    tval   df    pval   ci.lb   ci.ub      
  0.1636  0.0428  3.8232  158  0.0002  0.0791  0.2481  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod),2) # almost no phylogenetic effect

        I2_Total     I2_Phylogeny I2_Species_Latin         I2_Study 
           99.41             2.70            42.43            33.89 
    I2_Effect_ID 
           20.39 

# visualizing the result
orchard_plot(mod, xlab = "log response ratio (lnRR)", group = "Study")

# reduced model without phylogeny 
# alternative


mod2 <-  rma.mv(yi, V = V_matrix, mod = ~ 1, 
               random = list(#~1|Phylogeny, 
                             ~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               #R = list(Phylogeny = cor_tree), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(mod2) 

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.5131  -33.0262  -25.0262  -12.7758  -24.7648   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0172  0.1310     22     no  Species_Latin 
sigma^2.2  0.0132  0.1149     71     no          Study 
sigma^2.3  0.0080  0.0894    159     no      Effect_ID 

Test for Heterogeneity:
Q(df = 158) = 1366.5635, p-val < .0001

Model Results:

estimate      se    tval   df    pval   ci.lb   ci.ub      
  0.1589  0.0383  4.1529  158  <.0001  0.0833  0.2345  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# we will not use robust for the analysis - they do not seem to change the results
# rob2.2 <- robust(mod2, cluster = Study, adjust=TRUE, clubSandwich=TRUE, verbose=TRUE)
# rob2.2

anova(mod, mod2) # they are not significantly different

        df      AIC      BIC     AICc  logLik    LRT   pval        QE 
Full     5 -23.0384  -7.7254 -22.6436 16.5192               1366.5635 
Reduced  4 -25.0262 -12.7758 -24.7648 16.5131 0.0122 0.9122 1366.5635 

Alternative effect size

# using alternative effect size: -1/ln(p)

V_matrix2 <- impute_covariance_matrix(vi = dat$v2, cluster = dat$Shared_control, r = 0.5)

#
mod_alt <-  rma.mv(y2, V = V_matrix2, mod = ~ 1, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(mod_alt) 

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
-72.7948  145.5896  153.5896  165.8400  153.8511   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0394  0.1985     22     no  Species_Latin 
sigma^2.2  0.0137  0.1168     71     no          Study 
sigma^2.3  0.0025  0.0502    159     no      Effect_ID 

Test for Heterogeneity:
Q(df = 158) = 676.1277, p-val < .0001

Model Results:

estimate      se    tval   df    pval   ci.lb   ci.ub      
  0.2384  0.0602  3.9625  158  0.0001  0.1196  0.3573  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_alt),2) # almost no phylogenetic effect

        I2_Total I2_Species_Latin         I2_Study     I2_Effect_ID 
           98.06            69.52            24.09             4.45 

# visualizing the result
orchard_plot(mod_alt, xlab = "log response ratio (lnRR)", group = "Study")

7 Meta-regression: non-full models

Model function

# no phylogeny

mod_func <-  function(formula) {
        rma.mv(yi, 
               V = V_matrix,
               mod = formula, 
               random = list(#~1|Phylogeny, 
                             ~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
                            #optmethod="BFGS")
               )
}

Sex difference (Sex)

mod_sex <- mod_func(formula = ~ Sex-1)
summary(mod_sex)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 15.8527  -31.7053  -21.7053   -6.4241  -21.3080   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0170  0.1305     22     no  Species_Latin 
sigma^2.2  0.0132  0.1150     71     no          Study 
sigma^2.3  0.0082  0.0908    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1356.6488, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 8.6421, p-val = 0.0003

Model Results:

           estimate      se    tval   df    pval   ci.lb   ci.ub      
SexFemale    0.1581  0.0400  3.9528  157  0.0001  0.0791  0.2371  *** 
SexMale      0.1603  0.0441  3.6310  157  0.0004  0.0731  0.2475  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_sex1 <- mod_func(formula = ~ Sex)
summary(mod_sex1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 15.8527  -31.7053  -21.7053   -6.4241  -21.3080   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0170  0.1305     22     no  Species_Latin 
sigma^2.2  0.0132  0.1150     71     no          Study 
sigma^2.3  0.0082  0.0908    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1356.6488, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 0.0041, p-val = 0.9493

Model Results:

         estimate      se    tval   df    pval    ci.lb   ci.ub      
intrcpt    0.1581  0.0400  3.9528  157  0.0001   0.0791  0.2371  *** 
SexMale    0.0022  0.0339  0.0637  157  0.9493  -0.0648  0.0691      

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_sex1)

   R2_marginal R2_conditional 
  2.840973e-05   7.858099e-01 

# orchard plot
#test <- mod_results(mod_sex1, mod = "Sex", group = "Study", data = dat)
p1 <- orchard_plot(mod_sex1, mod = "Sex", 
                   xlab = "log response ratio (lnRR)", 
                   group = "Study",cb = F,
                   angle = 0)

p1

Gonad Removal (Sex_Gonads)

# creating a new variable Sex + Gonad because 

dat$Sex_Gonads <- paste(dat$Sex, dat$Gonads_removed, sep = "_")

dat$Sex_Gonads[grep("NA", dat$Sex_Gonads)] <- NA


dat$Sex_Gonads <- factor(dat$Sex_Gonads, 
       levels = c("Female_No", "Female_Yes", "Male_Yes"),
       labels = c("Gonads removed \n(female)", "Gonads not removed \n(female)", "Gonads removed \n(male)") 
       )


mod_rem <- mod_func(formula = ~ Sex_Gonads-1)
summary(mod_rem)

Multivariate Meta-Analysis Model (k = 155; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 13.3161  -26.6322  -14.6322    3.5111  -14.0528   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0226  0.1502     20     no  Species_Latin 
sigma^2.2  0.0115  0.1073     70     no          Study 
sigma^2.3  0.0092  0.0958    155     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 152) = 1320.7321, p-val < .0001

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 152) = 4.7873, p-val = 0.0032

Model Results:

                                         estimate      se    tval   df    pval 
Sex_GonadsGonads removed \n(female)        0.1345  0.0549  2.4481  152  0.0155 
Sex_GonadsGonads not removed \n(female)    0.1706  0.0493  3.4595  152  0.0007 
Sex_GonadsGonads removed \n(male)          0.1730  0.0506  3.4187  152  0.0008 
                                          ci.lb   ci.ub      
Sex_GonadsGonads removed \n(female)      0.0260  0.2431    * 
Sex_GonadsGonads not removed \n(female)  0.0732  0.2680  *** 
Sex_GonadsGonads removed \n(male)        0.0730  0.2729  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_rem1 <- mod_func(formula = ~ Sex_Gonads)
summary(mod_rem1)

Multivariate Meta-Analysis Model (k = 155; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 13.3161  -26.6322  -14.6322    3.5111  -14.0528   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0226  0.1502     20     no  Species_Latin 
sigma^2.2  0.0115  0.1073     70     no          Study 
sigma^2.3  0.0092  0.0958    155     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 152) = 1320.7321, p-val < .0001

Test of Moderators (coefficients 2:3):
F(df1 = 2, df2 = 152) = 0.2448, p-val = 0.7832

Model Results:

                                         estimate      se    tval   df    pval 
intrcpt                                    0.1345  0.0549  2.4481  152  0.0155 
Sex_GonadsGonads not removed \n(female)    0.0361  0.0540  0.6676  152  0.5054 
Sex_GonadsGonads removed \n(male)          0.0385  0.0585  0.6571  152  0.5121 
                                           ci.lb   ci.ub    
intrcpt                                   0.0260  0.2431  * 
Sex_GonadsGonads not removed \n(female)  -0.0707  0.1428    
Sex_GonadsGonads removed \n(male)        -0.0772  0.1541    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_rem1)

   R2_marginal R2_conditional 
   0.005389222    0.789101671 

# orchard plot
p2 <- orchard_plot(mod_rem, mod = "Sex_Gonads", 
                   xlab = "log response ratio (lnRR)", 
                   group = "Study", cb = F,
                   angle = 0) 

p2

Environmental (Wild_or_semi_wild)

dat$Wild_or_semi_wild <- factor(dat$Wild_or_semi_wild, 
       levels = c("No", "Yes"),
       labels = c("Others \n(e.g., lab, farm)" , "Wild or \nSemi-wild") 
       )


mod_env <- mod_func(formula = ~ Wild_or_semi_wild-1)
summary(mod_env)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 18.2939  -36.5878  -26.5878  -11.3066  -26.1904   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0163  0.1277     22     no  Species_Latin 
sigma^2.2  0.0155  0.1247     71     no          Study 
sigma^2.3  0.0065  0.0808    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1242.5261, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 10.8042, p-val < .0001

Model Results:

                                             estimate      se    tval   df 
Wild_or_semi_wildOthers \n(e.g., lab, farm)    0.1160  0.0434  2.6721  157 
Wild_or_semi_wildWild or \nSemi-wild           0.2379  0.0540  4.4056  157 
                                               pval   ci.lb   ci.ub      
Wild_or_semi_wildOthers \n(e.g., lab, farm)  0.0083  0.0302  0.2017   ** 
Wild_or_semi_wildWild or \nSemi-wild         <.0001  0.1313  0.3446  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_env1 <- mod_func(formula = ~ Wild_or_semi_wild)
summary(mod_env1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 18.2939  -36.5878  -26.5878  -11.3066  -26.1904   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0163  0.1277     22     no  Species_Latin 
sigma^2.2  0.0155  0.1247     71     no          Study 
sigma^2.3  0.0065  0.0808    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1242.5261, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 4.2887, p-val = 0.0400

Model Results:

                                      estimate      se    tval   df    pval 
intrcpt                                 0.1160  0.0434  2.6721  157  0.0083 
Wild_or_semi_wildWild or \nSemi-wild    0.1220  0.0589  2.0709  157  0.0400 
                                       ci.lb   ci.ub     
intrcpt                               0.0302  0.2017  ** 
Wild_or_semi_wildWild or \nSemi-wild  0.0056  0.2383   * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_env1)

   R2_marginal R2_conditional 
    0.06841806     0.84154080 

# orchard plot
p3 <- orchard_plot(mod_env1, mod = "Wild_or_semi_wild", 
                   xlab = "log response ratio (lnRR)", 
                   group = "Study") +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

p3

Controlled treatment (Controlled_treatments)

dat$Controlled_treatments <- factor(dat$Controlled_treatments, 
       levels = c("No", "Yes"),
       labels = c("Not controlled" , "Controlled") 
       )

mod_con <- mod_func(formula = ~ Controlled_treatments-1)
summary(mod_con)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 17.2675  -34.5349  -24.5349   -9.2537  -24.1376   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0181  0.1345     22     no  Species_Latin 
sigma^2.2  0.0120  0.1098     71     no          Study 
sigma^2.3  0.0082  0.0903    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1157.4537, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 9.1744, p-val = 0.0002

Model Results:

                                     estimate      se    tval   df    pval 
Controlled_treatmentsNot controlled    0.1127  0.0559  2.0162  157  0.0455 
Controlled_treatmentsControlled        0.1938  0.0490  3.9554  157  0.0001 
                                      ci.lb   ci.ub      
Controlled_treatmentsNot controlled  0.0023  0.2232    * 
Controlled_treatmentsControlled      0.0970  0.2906  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_con1 <- mod_func(formula = ~ Controlled_treatments)
summary(mod_con1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 17.2675  -34.5349  -24.5349   -9.2537  -24.1376   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0181  0.1345     22     no  Species_Latin 
sigma^2.2  0.0120  0.1098     71     no          Study 
sigma^2.3  0.0082  0.0903    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1157.4537, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 1.3130, p-val = 0.2536

Model Results:

                                 estimate      se    tval   df    pval    ci.lb 
intrcpt                            0.1127  0.0559  2.0162  157  0.0455   0.0023 
Controlled_treatmentsControlled    0.0810  0.0707  1.1459  157  0.2536  -0.0587 
                                  ci.ub    
intrcpt                          0.2232  * 
Controlled_treatmentsControlled  0.2207    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_con1)

   R2_marginal R2_conditional 
    0.04115198     0.79572638 

# orchard plot
p4 <- orchard_plot(mod_con1, mod = "Controlled_treatments", 
                   xlab = "log response ratio (lnRR)",
                   group = "Study") +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

p4

Sham (Shamtreatment_moderator)

dat$Shamtreatment_moderator <- factor(dat$Shamtreatment_moderator, 
       levels = c("No", "Yes"),
       labels = c("No sham" , "Sham-controlled") 
       )

mod_sham <- mod_func(formula = ~ Shamtreatment_moderator-1)
summary(mod_sham)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.9221  -33.8441  -23.8441   -8.5629  -23.4468   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0240  0.1549     22     no  Species_Latin 
sigma^2.2  0.0111  0.1052     71     no          Study 
sigma^2.3  0.0079  0.0891    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1210.4783, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 8.3174, p-val = 0.0004

Model Results:

                                        estimate      se    tval   df    pval 
Shamtreatment_moderatorNo sham            0.1376  0.0459  2.9957  157  0.0032 
Shamtreatment_moderatorSham-controlled    0.2009  0.0514  3.9071  157  0.0001 
                                         ci.lb   ci.ub      
Shamtreatment_moderatorNo sham          0.0469  0.2283   ** 
Shamtreatment_moderatorSham-controlled  0.0993  0.3024  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_sham1 <- mod_func(formula = ~ Shamtreatment_moderator)
summary(mod_sham1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.9221  -33.8441  -23.8441   -8.5629  -23.4468   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0240  0.1549     22     no  Species_Latin 
sigma^2.2  0.0111  0.1052     71     no          Study 
sigma^2.3  0.0079  0.0891    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1210.4783, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 1.7038, p-val = 0.1937

Model Results:

                                        estimate      se    tval   df    pval 
intrcpt                                   0.1376  0.0459  2.9957  157  0.0032 
Shamtreatment_moderatorSham-controlled    0.0633  0.0485  1.3053  157  0.1937 
                                          ci.lb   ci.ub     
intrcpt                                  0.0469  0.2283  ** 
Shamtreatment_moderatorSham-controlled  -0.0325  0.1590     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_sham1)

   R2_marginal R2_conditional 
    0.01935552     0.81917113 

# orchard plot
p5 <- orchard_plot(mod_sham, mod = "Shamtreatment_moderator", 
             xlab = "log response ratio (lnRR)", 
             group = "Study") +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

p5

Type of effect sizes (Effect_type)

dat$Effect_type <- factor(dat$Effect_type, 
       levels = c("longevity", "mortality"),
       labels = c("Mean or meadian \nlongevity" , "Mortality \n(%)") 
       )

mod_eff <- mod_func(formula = ~ Effect_type-1)
summary(mod_eff)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.8833  -33.7665  -23.7665   -8.4853  -23.3692   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0162  0.1271     22     no  Species_Latin 
sigma^2.2  0.0144  0.1202     71     no          Study 
sigma^2.3  0.0079  0.0888    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1201.4085, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 9.2756, p-val = 0.0002

Model Results:

                                        estimate      se    tval   df    pval 
Effect_typeMean or meadian \nlongevity    0.1222  0.0518  2.3584  157  0.0196 
Effect_typeMortality \n(%)                0.1784  0.0425  4.1970  157  <.0001 
                                         ci.lb   ci.ub      
Effect_typeMean or meadian \nlongevity  0.0198  0.2245    * 
Effect_typeMortality \n(%)              0.0944  0.2623  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# contrast
mod_eff1 <- mod_func(formula = ~ Effect_type)
summary(mod_eff1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.8833  -33.7665  -23.7665   -8.4853  -23.3692   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0162  0.1271     22     no  Species_Latin 
sigma^2.2  0.0144  0.1202     71     no          Study 
sigma^2.3  0.0079  0.0888    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1201.4085, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 1.0636, p-val = 0.3040

Model Results:

                            estimate      se    tval   df    pval    ci.lb 
intrcpt                       0.1222  0.0518  2.3584  157  0.0196   0.0198 
Effect_typeMortality \n(%)    0.0562  0.0545  1.0313  157  0.3040  -0.0514 
                             ci.ub    
intrcpt                     0.2245  * 
Effect_typeMortality \n(%)  0.1638    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_eff1)

   R2_marginal R2_conditional 
    0.02020104     0.79927113 

# orchard plot
p6 <- orchard_plot(mod_eff1, mod = "Effect_type", 
             xlab = "log response ratio (lnRR)", 
             group = "Study",cb = F) +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 
p6

mod_effa <- mod_alt1 <-  rma.mv(y2, V = V_matrix2,
              mod = ~ Effect_type-1, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead"))
summary(mod_effa)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
-71.1533  142.3065  152.3065  167.5878  152.7039   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0179  0.1337     22     no  Species_Latin 
sigma^2.2  0.0176  0.1327     71     no          Study 
sigma^2.3  0.0024  0.0492    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 587.4308, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 12.7018, p-val < .0001

Model Results:

                                        estimate      se    tval   df    pval 
Effect_typeMean or meadian \nlongevity    0.1538  0.0569  2.7030  157  0.0076 
Effect_typeMortality \n(%)                0.3168  0.0674  4.6983  157  <.0001 
                                         ci.lb   ci.ub      
Effect_typeMean or meadian \nlongevity  0.0414  0.2661   ** 
Effect_typeMortality \n(%)              0.1836  0.4500  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_effa1 <- mod_alt1 <-  rma.mv(y2, V = V_matrix2,
              mod = ~ Effect_type, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead"))
summary(mod_effa1)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
-71.1533  142.3065  152.3065  167.5878  152.7039   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0179  0.1337     22     no  Species_Latin 
sigma^2.2  0.0176  0.1327     71     no          Study 
sigma^2.3  0.0024  0.0492    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 587.4308, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 4.2265, p-val = 0.0415

Model Results:

                            estimate      se    tval   df    pval   ci.lb 
intrcpt                       0.1538  0.0569  2.7030  157  0.0076  0.0414 
Effect_typeMortality \n(%)    0.1631  0.0793  2.0559  157  0.0415  0.0064 
                             ci.ub     
intrcpt                     0.2661  ** 
Effect_typeMortality \n(%)  0.3198   * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_effa1)

   R2_marginal R2_conditional 
     0.1498655      0.9456636 

# orchard plot
orchard_plot(mod_effa1, mod = "Effect_type", 
             xlab = "log response ratio (lnRR)", 
             group = "Study",cb = F) +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

Matuarity (Maturity_at_treatment_ordinal)

mod_mat <- mod_func(formula = ~ Maturity_at_treatment_ordinal)
summary(mod_mat)

Multivariate Meta-Analysis Model (k = 100; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 -1.3998    2.7996   12.7996   25.7244   13.4517   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0134  0.1158     18     no  Species_Latin 
sigma^2.2  0.0294  0.1715     51     no          Study 
sigma^2.3  0.0028  0.0525    100     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 98) = 503.0467, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 98) = 2.2803, p-val = 0.1342

Model Results:

                               estimate      se     tval  df    pval    ci.lb 
intrcpt                          0.3557  0.1076   3.3052  98  0.0013   0.1421 
Maturity_at_treatment_ordinal   -0.0437  0.0289  -1.5101  98  0.1342  -0.1010 
                                ci.ub     
intrcpt                        0.5692  ** 
Maturity_at_treatment_ordinal  0.0137     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_mat)

   R2_marginal R2_conditional 
    0.04342106     0.94205047 

# bubble plot
p7 <- bubble_plot(mod_mat, 
                  xlab = "Life-course stages",
                  ylab = "lnRR (effect size)",
            mod = "Maturity_at_treatment_ordinal", 
            group = "Study", cb = F)

p7

Sex x Matuarity

# interaction with Sex
mod_sex_mat <- mod_func(formula = ~ Sex*Maturity_at_treatment_ordinal)
summary(mod_sex_mat)

Multivariate Meta-Analysis Model (k = 100; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  2.2987   -4.5974    9.4026   27.3531   10.6754   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0088  0.0940     18     no  Species_Latin 
sigma^2.2  0.0317  0.1781     51     no          Study 
sigma^2.3  0.0021  0.0454    100     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 96) = 494.5351, p-val < .0001

Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 96) = 3.8206, p-val = 0.0124

Model Results:

                                       estimate      se     tval  df    pval 
intrcpt                                  0.0319  0.1681   0.1897  96  0.8500 
SexMale                                  0.4940  0.1948   2.5358  96  0.0128 
Maturity_at_treatment_ordinal            0.0438  0.0442   0.9890  96  0.3251 
SexMale:Maturity_at_treatment_ordinal   -0.1681  0.0572  -2.9397  96  0.0041 
                                         ci.lb    ci.ub     
intrcpt                                -0.3018   0.3655     
SexMale                                 0.1073   0.8807   * 
Maturity_at_treatment_ordinal          -0.0441   0.1316     
SexMale:Maturity_at_treatment_ordinal  -0.2817  -0.0546  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_mat)

   R2_marginal R2_conditional 
    0.04342106     0.94205047 

# different reference (gives whether male slope is significant)
mod_sex_mat1 <- mod_func(formula = ~ relevel(Sex,ref = "Male")*Maturity_at_treatment_ordinal)
summary(mod_sex_mat1)

Multivariate Meta-Analysis Model (k = 100; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  2.2987   -4.5974    9.4026   27.3531   10.6754   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0088  0.0940     18     no  Species_Latin 
sigma^2.2  0.0317  0.1781     51     no          Study 
sigma^2.3  0.0021  0.0454    100     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 96) = 494.5351, p-val < .0001

Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 96) = 3.8206, p-val = 0.0124

Model Results:

                                                                estimate 
intrcpt                                                           0.5259 
relevel(Sex, ref = "Male")Female                                 -0.4940 
Maturity_at_treatment_ordinal                                    -0.1244 
relevel(Sex, ref = "Male")Female:Maturity_at_treatment_ordinal    0.1681 
                                                                    se     tval 
intrcpt                                                         0.1240   4.2405 
relevel(Sex, ref = "Male")Female                                0.1948  -2.5358 
Maturity_at_treatment_ordinal                                   0.0394  -3.1576 
relevel(Sex, ref = "Male")Female:Maturity_at_treatment_ordinal  0.0572   2.9397 
                                                                df    pval 
intrcpt                                                         96  <.0001 
relevel(Sex, ref = "Male")Female                                96  0.0128 
Maturity_at_treatment_ordinal                                   96  0.0021 
relevel(Sex, ref = "Male")Female:Maturity_at_treatment_ordinal  96  0.0041 
                                                                  ci.lb 
intrcpt                                                          0.2797 
relevel(Sex, ref = "Male")Female                                -0.8807 
Maturity_at_treatment_ordinal                                   -0.2026 
relevel(Sex, ref = "Male")Female:Maturity_at_treatment_ordinal   0.0546 
                                                                  ci.ub      
intrcpt                                                          0.7720  *** 
relevel(Sex, ref = "Male")Female                                -0.1073    * 
Maturity_at_treatment_ordinal                                   -0.0462   ** 
relevel(Sex, ref = "Male")Female:Maturity_at_treatment_ordinal   0.2817   ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# bubble plot
p8 <- bubble_plot(mod_sex_mat, 
                  xlab = "Life-course stages",
                  ylab = "lnRR (effect size)",
            mod = "Maturity_at_treatment_ordinal", 
            group = "Study", by = "Sex")

p8

8 Meta-regression: full model

Full model

# at least 10 in each group (sex * wild = male wild is too few - 4)

mod_full <- mod_func(formula = ~ Sex_Gonads + Sex*Controlled_treatments +  Wild_or_semi_wild + Sex*Maturity_at_treatment_ordinal)
summary(mod_full)

Multivariate Meta-Analysis Model (k = 100; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  1.1461   -2.2921   19.7079   47.4476   23.0079   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0156  0.1248     18     no  Species_Latin 
sigma^2.2  0.0291  0.1706     51     no          Study 
sigma^2.3  0.0027  0.0519    100     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 92) = 367.7087, p-val < .0001

Test of Moderators (coefficients 2:8):
F(df1 = 7, df2 = 92) = 1.6388, p-val = 0.1343

Model Results:

                                         estimate      se     tval  df    pval 
intrcpt                                   -0.0002  0.1932  -0.0013  92  0.9990 
Sex_GonadsGonads not removed \n(female)   -0.0020  0.0468  -0.0435  92  0.9654 
Sex_GonadsGonads removed \n(male)          0.5876  0.2374   2.4746  92  0.0152 
Controlled_treatmentsControlled            0.0390  0.0891   0.4376  92  0.6627 
Wild_or_semi_wildWild or \nSemi-wild      -0.0171  0.1008  -0.1694  92  0.8658 
Maturity_at_treatment_ordinal              0.0500  0.0467   1.0704  92  0.2873 
SexMale:Controlled_treatmentsControlled   -0.1090  0.1379  -0.7904  92  0.4313 
SexMale:Maturity_at_treatment_ordinal     -0.1720  0.0592  -2.9058  92  0.0046 
                                           ci.lb    ci.ub     
intrcpt                                  -0.3839   0.3834     
Sex_GonadsGonads not removed \n(female)  -0.0949   0.0908     
Sex_GonadsGonads removed \n(male)         0.1160   1.0591   * 
Controlled_treatmentsControlled          -0.1379   0.2158     
Wild_or_semi_wildWild or \nSemi-wild     -0.2174   0.1832     
Maturity_at_treatment_ordinal            -0.0428   0.1428     
SexMale:Controlled_treatmentsControlled  -0.3830   0.1649     
SexMale:Maturity_at_treatment_ordinal    -0.2896  -0.0544  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_full)

   R2_marginal R2_conditional 
     0.1331988      0.9507803 

AIC model selection

#res_mod_full <- dredge(mod_full, trace=2)
res_mod_full <- dredge(mod_full, trace=2)

saveRDS(res_mod_full, file = here("Rdata", "literature", "res_mod_full.rds"))

res_mod_full <- readRDS(file = here("Rdata","literature", "res_mod_full.rds"))

# delta AIC = 2
res_mod_full2<- subset(res_mod_full, delta <= 2) #, recalc.weights=FALSE)

# the best model according to the delta 2
best2 <- mod_func(formula = ~ Controlled_treatments + Wild_or_semi_wild)
#summary(best2)

# model varaged coeffisents
avg2 <- model.avg(res_mod_full2)
summary(avg2) # similar to the orignal resulde

Call:
model.avg(object = res_mod_full2)

Component model call: 
rma.mv(yi = yi, V = V_matrix, mods = ~<4 unique rhs>, random = list(~1 
     | Species_Latin, ~1 | Study, ~1 | Effect_ID), data = dat, test = t, 
     sparse = TRUE, control = list(optimizer = "optim", optmethod = 
     "Nelder-Mead"))

Component models: 
       df logLik   AICc delta weight
2       5  14.84 -19.28  0.00   0.38
(Null)  4  13.29 -18.32  0.96   0.24
12      6  15.25 -17.95  1.34   0.20
1       5  14.10 -17.80  1.48   0.18

Term codes: 
Controlled_treatments     Wild_or_semi_wild 
                    1                     2 

Model-averaged coefficients:  
(full average) 
                                     Estimate Std. Error z value Pr(>|z|)  
intrcpt                               0.12316    0.05473   2.250   0.0244 *
Wild_or_semi_wildWild or \nSemi-wild  0.06579    0.07279   0.904   0.3661  
Controlled_treatmentsYes              0.02739    0.05755   0.476   0.6341  
 
(conditional average) 
                                     Estimate Std. Error z value Pr(>|z|)  
intrcpt                               0.12316    0.05473   2.250   0.0244 *
Wild_or_semi_wildWild or \nSemi-wild  0.11342    0.06109   1.856   0.0634 .
Controlled_treatmentsYes              0.07219    0.07412   0.974   0.3301  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# note 
# controlling and wild-semi-wide is correlated: r = 0.34
#cor.test(as.numeric(dat$Controlled_treatments),as.numeric(dat$Wild_or_semi_wild))

9 Publication bais & sensitivity analysis

Funnel plot

# raw funnel plot
# funnel plot - 
funnel(mod)

# residual funnel plot
funnel(best2)

Small-study effect: uni-moderaotor

dat$Effective_N <- 1/dat$Sample_size_sterilization + 1/dat$Sample_size_control

egger_uni <- mod_func(formula = ~ sqrt(Effective_N))
#egger_uni2 <- mod_func(formula = ~ Effective_N)

summary(egger_uni)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 19.6267  -39.2535  -29.2535  -13.9722  -28.8561   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0186  0.1362     22     no  Species_Latin 
sigma^2.2  0.0067  0.0818     71     no          Study 
sigma^2.3  0.0103  0.1017    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1244.6556, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 7.9120, p-val = 0.0055

Model Results:

                   estimate      se    tval   df    pval    ci.lb   ci.ub     
intrcpt              0.0546  0.0525  1.0397  157  0.3001  -0.0491  0.1582     
sqrt(Effective_N)    0.5685  0.2021  2.8128  157  0.0055   0.1693  0.9677  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# 
egger_uni2 <- mod_func(formula = ~ Effective_N)
summary(egger_uni2)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.3751  -40.7503  -30.7503  -15.4691  -30.3529   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0162  0.1272     22     no  Species_Latin 
sigma^2.2  0.0081  0.0902     71     no          Study 
sigma^2.3  0.0095  0.0976    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1240.7008, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 8.9310, p-val = 0.0033

Model Results:

             estimate      se    tval   df    pval   ci.lb   ci.ub     
intrcpt        0.0963  0.0415  2.3226  157  0.0215  0.0144  0.1782   * 
Effective_N    1.3131  0.4394  2.9885  157  0.0033  0.4452  2.1809  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Decline effect (time lag bias): uni-moderator

dat$Year <- as.numeric(str_extract(as.character(dat$Study),"[:digit:][:digit:][:digit:][:digit:]"))

decline_uni <- mod_func(formula = ~ Year)
summary(decline_uni)

Multivariate Meta-Analysis Model (k = 159; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 16.4273  -32.8546  -22.8546   -7.5734  -22.4573   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0170  0.1304     22     no  Species_Latin 
sigma^2.2  0.0138  0.1175     71     no          Study 
sigma^2.3  0.0080  0.0893    159     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 157) = 1313.2195, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 0.0057, p-val = 0.9397

Model Results:

         estimate      se     tval   df    pval    ci.lb   ci.ub    
intrcpt    0.3555  2.5932   0.1371  157  0.8911  -4.7666  5.4776    
Year      -0.0001  0.0013  -0.0758  157  0.9397  -0.0027  0.0025    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Leave-one-study-out analysis

# The function for leave-one-study-out

dat$Study <- as.factor(dat$Study)

LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study))){
  dat1 <- dat[dat$Study != levels(dat$Study)[i], ]
  V_matrix <- impute_covariance_matrix(vi = dat1$vi, cluster = dat1$Shared_control, r = 0.5)
  
  LeaveOneOut_effectsize[[i]] <- rma.mv(yi, 
               V = V_matrix,
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="BFGS"),
               data = dat1)
  }


# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod){
  df <- data.frame(est = mod$b, lower = mod$ci.lb, upper = mod$ci.ub)
  return(df)
}

#using dplyr to form data frame
MA_LOO <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study))


saveRDS(MA_LOO,file = here("Rdata", "literature","MA_LOO.rds"))

#telling ggplot to stop reordering factors
MA_LOO <- readRDS(file = here("Rdata",  "literature", "MA_LOO.rds"))

MA_LOO$left_out<- as.factor(MA_LOO$left_out)
MA_LOO$left_out<-factor(MA_LOO$left_out, levels = MA_LOO$left_out)


#plotting
leaveoneout_E <- ggplot(MA_LOO) +
  geom_hline(yintercept = 0, lty = 2, lwd = 1) +
  geom_hline(yintercept = mod$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod$b, lty = 1, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
  geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
  xlab("Study left out") + 
  ylab("lnRR, 95% CI") + 
  coord_flip() +
  theme(panel.grid.minor = element_blank())+
  theme_bw() + theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor.x = element_blank() ) +
  theme(axis.text.y = element_text(size = 6))

leaveoneout_E

10 Meta-analysis: rodent data only

We note that the analyses below using rodent data are post-host analyses.

Main model

# shared control 
# this does not seem to work
#V_matrix <- make_VCV_matrix(dat, V= "vi", cluster = "Shared_control", obs = "Effect_ID")
V_matrix <- impute_covariance_matrix(vi = rdat$vi, cluster = rdat$Shared_control, r = 0.5)

# meta-analysis basics
# phylo model
rmod <-  rma.mv(yi, V = V_matrix, mod = ~ 1, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = rdat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(rmod) 

Multivariate Meta-Analysis Model (k = 40; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 23.9404  -47.8807  -39.8807  -33.2265  -38.7043   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0018  0.0423      3     no  Species_Latin 
sigma^2.2  0.0089  0.0941     23     no          Study 
sigma^2.3  0.0027  0.0522     40     no      Effect_ID 

Test for Heterogeneity:
Q(df = 39) = 176.6345, p-val < .0001

Model Results:

estimate      se    tval  df    pval    ci.lb   ci.ub    
  0.0650  0.0380  1.7128  39  0.0947  -0.0118  0.1418  . 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

i2_ml(rmod) 

        I2_Total I2_Species_Latin         I2_Study     I2_Effect_ID 
        86.13553         11.50770         57.04649         17.58134 

# visualizing the result
orchard_plot(rmod, xlab = "log response ratio (lnRR)", group = "Study")

Sex difference (Sex)

rmod_sex <-  rma.mv(yi, V = V_matrix, mod = ~ Sex, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = rdat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(rmod_sex) 

Multivariate Meta-Analysis Model (k = 40; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 22.6144  -45.2289  -35.2289  -27.0409  -33.3539   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0017  0.0414      3     no  Species_Latin 
sigma^2.2  0.0088  0.0936     23     no          Study 
sigma^2.3  0.0029  0.0543     40     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 38) = 164.4126, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 38) = 0.0501, p-val = 0.8241

Model Results:

         estimate      se    tval  df    pval    ci.lb   ci.ub    
intrcpt    0.0617  0.0403  1.5290  38  0.1345  -0.0200  0.1434    
SexMale    0.0074  0.0333  0.2238  38  0.8241  -0.0599  0.0748    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(rmod_sex) 

   R2_marginal R2_conditional 
   0.001033388    0.780759740 

# visualizing the result
orchard_plot(rmod_sex, mod = "Sex", 
             xlab = "log response ratio (lnRR)", 
             group = "Study")

Maturity (in days)

# two more type of ages
rdat$lnAge_Trt <- log(rdat$Age_at_treatment_continuous)
rdat$Age_ratio <- (rdat$Age_at_treatment_continuous/rdat$Day_to_matuarity)
rdat$lnAge_ratio <- log(rdat$Age_at_treatment_continuous/rdat$Day_to_matuarity)


# just pure effect
rmod_mat <- rma.mv(yi, V = V_matrix, mod = ~ lnAge_ratio*Sex, 
               random = list(~1|Species_Latin, 
                             ~1|Study, 
                             ~1|Effect_ID), 
               data = rdat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
               )
summary(rmod_mat)

Multivariate Meta-Analysis Model (k = 40; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 21.8680  -43.7360  -29.7360  -18.6514  -25.7360   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0006  0.0237      3     no  Species_Latin 
sigma^2.2  0.0108  0.1037     23     no          Study 
sigma^2.3  0.0016  0.0396     40     no      Effect_ID 

Test for Residual Heterogeneity:
QE(df = 36) = 147.1755, p-val < .0001

Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 36) = 1.7831, p-val = 0.1678

Model Results:

                     estimate      se     tval  df    pval    ci.lb   ci.ub    
intrcpt                0.0590  0.0347   1.7003  36  0.0977  -0.0114  0.1294  . 
lnAge_ratio           -0.0192  0.0152  -1.2562  36  0.2171  -0.0501  0.0118    
SexMale                0.0037  0.0327   0.1137  36  0.9101  -0.0626  0.0701    
lnAge_ratio:SexMale   -0.0102  0.0201  -0.5056  36  0.6162  -0.0510  0.0307    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(rmod_mat)

   R2_marginal R2_conditional 
    0.09196419     0.88970527 

# visualizing the result
bubble_plot(rmod_mat, mod = "lnAge_ratio", group = "Study", 
            by = "Sex", 
            xlab = "ln(Treatment Day/Day to sexual maturity) [Rodent data only]", 
            ylab = "ln(Response ratio)")

11 Contrasting sexes: full data

Calcuating aboslute effect sizes

# full data
dat_long <- dat_long %>% mutate(abs_yi = folded_mu(yi, vi), 
                      abs_vi = folded_v(yi, vi))

# partial data
sdat_long <- sdat_long %>% mutate(abs_yi = folded_mu(yi, vi), 
                      abs_vi = folded_v(yi, vi))

Comparing M/F vs. M\(\star\)/F or M/F\(\star\)

# variance covariance matrix
V_matrix_long <- impute_covariance_matrix(vi = dat_long$vi, cluster = dat_long$Shared_control, r = 0.5)

# we can run - some heteroscad models
# this does not improve model
mod_comp <-  rma.mv(yi, V = V_matrix_long, 
                 mod = ~ Comp_type - 1, 
                 random = list(~1|Species_Latin, 
                               ~1|Study, 
                               ~1|Effect_ID, 
                               ~1|Obs ), 
                 test = "t",
                 sparse = TRUE,
                 data = dat_long)
summary(mod_comp) 

Multivariate Meta-Analysis Model (k = 170; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  5.5127  -11.0253    0.9747   19.7184    1.4964   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0191  0.1381     15     no  Species_Latin 
sigma^2.2  0.0000  0.0000     34     no          Study 
sigma^2.3  0.0000  0.0000     85     no      Effect_ID 
sigma^2.4  0.0223  0.1492    170     no            Obs 

Test for Residual Heterogeneity:
QE(df = 168) = 1500.6613, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 168) = 0.1365, p-val = 0.8725

Model Results:

                        estimate      se    tval   df    pval    ci.lb   ci.ub 
Comp_typeboth_normal      0.0171  0.0454  0.3758  168  0.7076  -0.0726  0.1068 
Comp_typeone_castrated    0.0037  0.0450  0.0830  168  0.9340  -0.0851  0.0926 
                          
Comp_typeboth_normal      
Comp_typeone_castrated    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_comp)

   R2_marginal R2_conditional 
   0.001080811    0.462158653 

# visualizing the result
orchard_plot(mod_comp, mod = "Comp_type", xlab = "log response ratio (lnRR)", 
             group = "Study", 
             cb = F)

Separating by sex (M/F vs. M\(\star\)/F or M/F\(\star\))

# naming factors
# dat_long$Comp_type <-  factor(dat_long$Comp_type, 
#                                  levels = c("one_castrated", "both_normal"),
#                                  labels = c("one_castrated", "both_normal") )

dat_long$Comp_type_Sex1 <- factor(dat_long$Comp_type_Sex, 
                                 levels = c("one_castrated_Male", "both_normal_Male",
                                            "one_castrated_Female", "both_normal_Female"),
                                 labels = c("Male sterlized/\nFemale normal",
                                            "Male normal/\nFemale normal",
                                            "Male normal/\nFemale sterlized",
                                            "Male normal/\nFemale normal (B)") )

mod_comp_sex <-  rma.mv(yi, V = V_matrix_long, mod = ~ Comp_type_Sex1 -1 , random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs ),data = dat_long, test = "t")
summary(mod_comp_sex) 

Multivariate Meta-Analysis Model (k = 170; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 24.2082  -48.4164  -32.4164   -7.5205  -31.4992   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0191  0.1382     15     no  Species_Latin 
sigma^2.2  0.0000  0.0000     34     no          Study 
sigma^2.3  0.0000  0.0000     85     no      Effect_ID 
sigma^2.4  0.0142  0.1190    170     no            Obs 

Test for Residual Heterogeneity:
QE(df = 166) = 1158.6282, p-val < .0001

Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 166) = 11.6896, p-val < .0001

Model Results:

                                               estimate      se     tval   df 
Comp_type_Sex1Male sterlized/\nFemale normal     0.1297  0.0481   2.6949  166 
Comp_type_Sex1Male normal/\nFemale normal       -0.0115  0.0486  -0.2370  166 
Comp_type_Sex1Male normal/\nFemale sterlized    -0.1063  0.0476  -2.2332  166 
Comp_type_Sex1Male normal/\nFemale normal (B)    0.0518  0.0481   1.0774  166 
                                                 pval    ci.lb    ci.ub     
Comp_type_Sex1Male sterlized/\nFemale normal   0.0078   0.0347   0.2247  ** 
Comp_type_Sex1Male normal/\nFemale normal      0.8129  -0.1075   0.0844     
Comp_type_Sex1Male normal/\nFemale sterlized   0.0269  -0.2002  -0.0123   * 
Comp_type_Sex1Male normal/\nFemale normal (B)  0.2829  -0.0432   0.1468     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_comp_sex)

   R2_marginal R2_conditional 
     0.1848997      0.6530464 

d0 <- orchard_plot(mod_comp_sex, mod = "Comp_type_Sex1", xlab = "log response ratio (lnRR)", 
                   group = "Study", 
                   cb = T, angle = 45) +
  geom_vline(xintercept=2.5, size = 0.2)

d0

# plotting just male analyses - adjusting mod_comp_sex

res_sex <- mod_results(mod_comp_sex, mod = "Comp_type_Sex1", group = "Study")

attr(res_sex, "class") <- NULL

res_sex$mod_table <- res_sex$mod_table[1:2, ]
res_sex$data <- res_sex$data %>% filter(moderator == "Male sterlized/\nFemale normal"| moderator ==  "Male normal/\nFemale normal") 

res_sex2 <- res_sex

class(res_sex2) <-  c("orchard", "data.frame")

d01 <- orchard_plot(res_sex2, mod = "Comp_type_Sex1", xlab = "log response ratio (lnRR)", 
                   group = "Study", 
                   cb = T, flip = FALSE) 

d01

Absolute effect size comparaion (M/F or F/M vs. M\(\star\)/F or F\(\star\)/M)

# VCV matrix
abs_V_matrix_long <- impute_covariance_matrix(vi = dat_long$abs_vi, cluster = dat_long$Shared_control, r = 0.5)

abs_mod_comp_sex0 <-  rma.mv(abs_yi, V = abs_V_matrix_long, mod = ~ Comp_type_Sex1 , 
                             random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs ),
                             data = dat_long,  test = "t",
                 sparse = TRUE)
summary(abs_mod_comp_sex0) 

Multivariate Meta-Analysis Model (k = 170; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  95.4401  -190.8802  -174.8802  -149.9843  -173.9630   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0048  0.0690     15     no  Species_Latin 
sigma^2.2  0.0012  0.0350     34     no          Study 
sigma^2.3  0.0000  0.0000     85     no      Effect_ID 
sigma^2.4  0.0044  0.0662    170     no            Obs 

Test for Residual Heterogeneity:
QE(df = 166) = 929.3916, p-val < .0001

Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 166) = 10.3634, p-val < .0001

Model Results:

                                               estimate      se     tval   df 
intrcpt                                          0.1501  0.0283   5.3088  166 
Comp_type_Sex1Male normal/\nFemale normal        0.0139  0.0219   0.6366  166 
Comp_type_Sex1Male normal/\nFemale sterlized     0.0995  0.0262   3.7999  166 
Comp_type_Sex1Male normal/\nFemale normal (B)   -0.0077  0.0262  -0.2939  166 
                                                 pval    ci.lb   ci.ub      
intrcpt                                        <.0001   0.0943  0.2059  *** 
Comp_type_Sex1Male normal/\nFemale normal      0.5252  -0.0292  0.0571      
Comp_type_Sex1Male normal/\nFemale sterlized   0.0002   0.0478  0.1511  *** 
Comp_type_Sex1Male normal/\nFemale normal (B)  0.7692  -0.0594  0.0440      

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

abs_mod_comp_sex <-  rma.mv(abs_yi, V = abs_V_matrix_long, mod = ~ Comp_type_Sex1 -1 , 
                            random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs), 
                            data = dat_long,  test = "t",
                 sparse = TRUE)
summary(abs_mod_comp_sex) 

Multivariate Meta-Analysis Model (k = 170; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  95.4401  -190.8802  -174.8802  -149.9843  -173.9630   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0048  0.0690     15     no  Species_Latin 
sigma^2.2  0.0012  0.0350     34     no          Study 
sigma^2.3  0.0000  0.0000     85     no      Effect_ID 
sigma^2.4  0.0044  0.0662    170     no            Obs 

Test for Residual Heterogeneity:
QE(df = 166) = 929.3916, p-val < .0001

Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 166) = 21.0998, p-val < .0001

Model Results:

                                               estimate      se    tval   df 
Comp_type_Sex1Male sterlized/\nFemale normal     0.1501  0.0283  5.3088  166 
Comp_type_Sex1Male normal/\nFemale normal        0.1640  0.0287  5.7199  166 
Comp_type_Sex1Male normal/\nFemale sterlized     0.2496  0.0284  8.8009  166 
Comp_type_Sex1Male normal/\nFemale normal (B)    0.1424  0.0282  5.0412  166 
                                                 pval   ci.lb   ci.ub      
Comp_type_Sex1Male sterlized/\nFemale normal   <.0001  0.0943  0.2059  *** 
Comp_type_Sex1Male normal/\nFemale normal      <.0001  0.1074  0.2206  *** 
Comp_type_Sex1Male normal/\nFemale sterlized   <.0001  0.1936  0.3055  *** 
Comp_type_Sex1Male normal/\nFemale normal (B)  <.0001  0.0866  0.1982  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(abs_mod_comp_sex)

   R2_marginal R2_conditional 
     0.1548316      0.6429652 

# visualizing results
d1 <- orchard_plot(abs_mod_comp_sex, mod = "Comp_type_Sex1", xlab = "absolute log response ratio (lnRR)", 
                   group = "Study", 
                   cb = T, angle = 45) +
  geom_vline(xintercept=2.5, size = 0.2)

d1

# plotting just male analyses - adjusting mod_comp_sex

res_sex_abs <- mod_results(abs_mod_comp_sex, mod = "Comp_type_Sex1", group = "Study")

attr(res_sex_abs, "class") <- NULL

res_sex_abs$mod_table <- res_sex_abs$mod_table[1:2, ]
res_sex_abs$data <- res_sex_abs$data %>% filter(moderator == "Male sterlized/\nFemale normal"| moderator ==  "Male normal/\nFemale normal") 

res_sex_abs2 <- res_sex_abs

class(res_sex_abs2) <-  c("orchard", "data.frame")

d11 <- orchard_plot(res_sex_abs2, mod = "Comp_type_Sex1", xlab = "log response ratio (lnRR)", 
                   group = "Study", 
                   cb = T, flip = FALSE) 

d11

d01 / d11

# saving results for figs rds

saveRDS(res_sex2, file = here("Rdata", "fig", "res_sex_ma.rds"))
saveRDS(res_sex_abs2, file = here("Rdata", "fig", "res_sex_ma_abs.rds"))

Contrasting M/F and M/F\(\star\)

# female
female_dat_log <- dat_long %>% filter(Sex == "Female")
f_dat_log<- female_dat_log %>% group_by(Effect_ID) %>% summarise(yi2 = abs_yi[2] - abs_yi[1],
                                                     vi2 = abs_vi[1] + abs_vi[2] -
                                                       0.5*sqrt(abs_vi[1]*abs_vi[2]),
                                                     Species_Latin = Species_Latin[1],
                                                     Study = Study[1],
                                                     Shared_control = Shared_control[1])

# variance covariance matrix
V_matrix_long1 <- impute_covariance_matrix(vi = f_dat_log$vi2, cluster = f_dat_log$Shared_control, r = 0.5)

# we can run - some heteroscad models
# this does not improve model
f_mod_long <-  rma.mv(yi2, V = V_matrix_long1, 
                 random = list(~1|Species_Latin, 
                               ~1|Study, 
                               ~1|Effect_ID), 
                 data = f_dat_log, test = "t", sparse = TRUE)
summary(f_mod_long) 

Multivariate Meta-Analysis Model (k = 44; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 23.2097  -46.4193  -38.4193  -31.3745  -37.3667   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0045  0.0672     13     no  Species_Latin 
sigma^2.2  0.0000  0.0000     28     no          Study 
sigma^2.3  0.0035  0.0595     44     no      Effect_ID 

Test for Heterogeneity:
Q(df = 43) = 168.9263, p-val < .0001

Model Results:

estimate      se    tval  df    pval   ci.lb   ci.ub     
  0.0904  0.0320  2.8227  43  0.0072  0.0258  0.1549  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

i2_ml(f_mod_long)

        I2_Total I2_Species_Latin         I2_Study     I2_Effect_ID 
    7.053045e+01     3.952129e+01     4.435257e-07     3.100916e+01 

# Pair figures
d2 <- ggplot(female_dat_log, aes(x = Comp_type, y = abs_yi)) + 
  geom_point(aes(size = sqrt(1/abs_vi), col = Comp_type), alpha = 0.5) +
  geom_line(aes(group = Effect_ID), alpha = 0.5) + 
  labs(y = "absolute log response ratio (lnRR)", x = "", size = "Precision (1/SE)") + 
  scale_x_discrete(labels = c("one_castrated" ="Male normal/\nFemale sterlized", "both_normal" = "Male normal/\nFemale normal")) +
  #xlim(c("one_castrated","both_normal"))+
  ylim(0, 1.25) + 
  scale_color_manual(values = c("#DDCC77", "#117733")) +
  coord_flip() +
  theme_bw() +
  guides(colour = "none") +
  theme(legend.position= c(1, 0), legend.justification = c(1, 0)) +
    theme(legend.title = element_text(size = 9)) +
    theme(legend.direction="horizontal") +
    theme(legend.background = element_blank()) +
    theme(axis.text.y = element_text(size = 10, colour ="black",
                                                            hjust = 0.5,
                                                            angle = 90))

d2

d3 <- orchard_plot(f_mod_long, 
                   xlab = "absolute log response ratio (lnRR)", 
                   group = "Study", 
                   cb = F, angle = 90) + 
  scale_fill_manual(values =  "#999933") +
  scale_colour_manual(values =  "#999933") +
  scale_x_discrete(labels = "Male normal/Female sterlized vs. \nMale normal/Female normal") +
  ylim(-0.9, 0.5)
d3

Contrasting M/F and M\(\star\)/F

# pair figure male  
male_dat_log <- dat_long %>% filter(Sex == "Male")

m_dat_log<- male_dat_log %>% group_by(Effect_ID) %>% summarise(yi2 = abs_yi[2] - abs_yi[1],
                                                     vi2 = abs_vi[1] + abs_vi[2] -
                                                       0.5*sqrt(abs_vi[1]*abs_vi[2]),
                                                     Species_Latin = Species_Latin[1],
                                                     Study = Study[1],
                                                     Shared_control = Shared_control[1])

# variance covariance matrix
V_matrix_long2 <- impute_covariance_matrix(vi = m_dat_log$vi2, cluster = m_dat_log$Shared_control, r = 0.5)

# we can run - some hetero-scad models
# this does not improve model
m_mod_long <-  rma.mv(yi2, V = V_matrix_long2, 
                 random = list(~1|Species_Latin, 
                               ~1|Study, 
                               ~1|Effect_ID), 
                 data = m_dat_log, test = "t")
summary(m_mod_long) 

Multivariate Meta-Analysis Model (k = 41; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 12.6521  -25.3041  -17.3041  -10.5486  -16.1613   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0157  0.1252     12     no  Species_Latin 
sigma^2.2  0.0003  0.0185     28     no          Study 
sigma^2.3  0.0019  0.0441     41     no      Effect_ID 

Test for Heterogeneity:
Q(df = 40) = 96.4195, p-val < .0001

Model Results:

estimate      se     tval  df    pval    ci.lb   ci.ub    
 -0.0221  0.0462  -0.4784  40  0.6350  -0.1154  0.0712    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

i2_ml(m_mod_long)

        I2_Total I2_Species_Latin         I2_Study     I2_Effect_ID 
       90.745727        79.186792         1.729119         9.829816 

d4 <- ggplot(male_dat_log, aes(x =Comp_type, y = abs_yi)) + 
  geom_point(aes(size = sqrt(1/abs_vi), col = Comp_type), alpha = 0.5) +
  geom_line(aes(group = Effect_ID), alpha = 0.5) + 
  labs(y = "absolute log response ratio (lnRR)", x = "", size = "Precision (1/SE)") + 
  scale_x_discrete(labels = c("one_castrated" ="Male sterlized/\nFemale normal", "both_normal" = "Male normal/\nFemale normal")) +
  ylim(0, 1.25) + 
  #coord_cartesian(xlim = c(0.5, 2.5)) +
  scale_color_manual(values = c("#88CCEE", "#CC6677")) +
  coord_flip() +
  theme_bw() +
  guides(colour = "none") +
  theme(legend.position= c(1, 0), legend.justification = c(1, 0)) +
    theme(legend.title = element_text(size = 9)) +
    theme(legend.direction="horizontal") +
    theme(legend.background = element_blank()) +
    theme(axis.text.y = element_text(size = 10, colour ="black",
                                                            hjust = 0.5,
                                                            angle = 90))

d4 

d5 <- orchard_plot(m_mod_long, xlab = "absolute log response ratio (lnRR)", 
                   group = "Study", 
                   cb = F, angle = 90) + 
   scale_fill_manual(values = "#332288") +
  scale_colour_manual(values = "#332288") +
scale_x_discrete(labels = "Male sterlized/Female normal vs. \nMale normal/Female normal") +
  ylim(-0.9, 0.5)

d5

Figure: orchard plots - row and aboslute

patch <- d0 + d1 + plot_layout()

patch +  plot_annotation(tag_levels = "A")

Figure: contrasting and orchard plots

Related figures

patch2 <- (d2/d4)| (d3/d5) + plot_layout()

patch2 +  plot_annotation(tag_levels = "A")

12 Contrasting sexes: all-combination data

Comparing M/F vs. M\(\star\)/F\(\star\)

# naming factor
sdat_long$Comp_type <-  factor(sdat_long$Comp_type, 
                                 levels = c("both_castrated", "both_normal"),
                                 labels = c("Male sterlized/\nFemale sterlized",
                                            "Male normal/\nFemale normal") )

# VCV matrix
V_matrix_long <- impute_covariance_matrix(vi = sdat_long$vi, cluster = sdat_long$Shared_control, r = 0.5)

# correlaiton matrix for phylogeny
# tree <- read.tree(here("data/tree_rotl.tre"))
# tree <- compute.brlen(tree)
# cor_tree <- vcv(tree, corr = TRUE)

# without heteroscedasticity
mod_comp2 <-  rma.mv(yi, V = V_matrix_long, mod = ~ Comp_type - 1, random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs),data = sdat_long, test = "t")
summary(mod_comp2) 

Multivariate Meta-Analysis Model (k = 64; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 24.7648  -49.5296  -37.5296  -24.7668  -36.0023   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0125  0.1118     10     no  Species_Latin 
sigma^2.2  0.0000  0.0000     25     no          Study 
sigma^2.3  0.0033  0.0571     32     no      Effect_ID 
sigma^2.4  0.0154  0.1242     64     no            Obs 

Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 62) = 0.1994, p-val = 0.8197

Model Results:

                                            estimate      se     tval  df 
Comp_typeMale sterlized/\nFemale sterlized   -0.0104  0.0482  -0.2156  62 
Comp_typeMale normal/\nFemale normal         -0.0264  0.0482  -0.5470  62 
                                              pval    ci.lb   ci.ub    
Comp_typeMale sterlized/\nFemale sterlized  0.8300  -0.1067  0.0860    
Comp_typeMale normal/\nFemale normal        0.5863  -0.1227  0.0700    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# without heteroscedasticity
mod_comp2b <-  rma.mv(yi, V = V_matrix_long, mod = ~ Comp_type, random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs),data = sdat_long, test = "t")
summary(mod_comp2b) 

Multivariate Meta-Analysis Model (k = 64; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 24.7648  -49.5296  -37.5296  -24.7668  -36.0023   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0125  0.1118     10     no  Species_Latin 
sigma^2.2  0.0000  0.0000     25     no          Study 
sigma^2.3  0.0033  0.0571     32     no      Effect_ID 
sigma^2.4  0.0154  0.1242     64     no            Obs 

Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 0.2342, p-val = 0.6301

Model Results:

                                      estimate      se     tval  df    pval 
intrcpt                                -0.0104  0.0482  -0.2156  62  0.8300 
Comp_typeMale normal/\nFemale normal   -0.0160  0.0330  -0.4839  62  0.6301 
                                        ci.lb   ci.ub    
intrcpt                               -0.1067  0.0860    
Comp_typeMale normal/\nFemale normal  -0.0820  0.0500    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

r2_ml(mod_comp2b)

   R2_marginal R2_conditional 
   0.002076156    0.506405183 

# with heteroscedasticity
mod_comp2c <-  rma.mv(yi, V = V_matrix_long, 
                     mod = ~ Comp_type, 
                     random = list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),
                    rho = 0, struct = "HCS",
                     data = sdat_long, test = "t")
summary(mod_comp2c) 

Multivariate Meta-Analysis Model (k = 64; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 30.3689  -60.7377  -48.7377  -35.9749  -47.2105   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0057  0.0756     10     no  Species_Latin 
sigma^2.2  0.0000  0.0000     25     no          Study 

outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)

            estim    sqrt  k.lvl  fixed                              level 
tau^2.1    0.0058  0.0763     32     no  Male sterlized/\nFemale sterlized 
tau^2.2    0.0343  0.1851     32     no        Male normal/\nFemale normal 
rho        0.0000                   yes                                    

Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 0.1620, p-val = 0.6887

Model Results:

                                      estimate      se     tval  df    pval 
intrcpt                                -0.0231  0.0330  -0.7005  62  0.4862 
Comp_typeMale normal/\nFemale normal   -0.0150  0.0374  -0.4025  62  0.6887 
                                        ci.lb   ci.ub    
intrcpt                               -0.0889  0.0428    
Comp_typeMale normal/\nFemale normal  -0.0897  0.0596    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Comparing models with and without heteroscedasticity
AIC(mod_comp2, mod_comp2c)

           df       AIC
mod_comp2   6 -37.52956
mod_comp2c  6 -48.73773

# visualizing results
f1 <- orchard_plot(mod_comp2c, mod = "Comp_type", 
                   xlab = "log response ratio (lnRR)", 
                   group = "Study", 
                   angle = 45) +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

f1

Absolute effect size comparaion (M/F vs. M\(\star\)/F\(\star\))

# VCV matrix
abs_V_matrix_long <- impute_covariance_matrix(vi = sdat_long$abs_vi, cluster = sdat_long$Shared_control, r = 0.5)


# with heteroscedasticity
mod_comp3 <-  rma.mv(abs_yi, V = abs_V_matrix_long, 
                     mod = ~ Comp_type - 1, 
                     random = list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),
                    rho = 0, struct = "HCS",
                     data = sdat_long, test = "t")
summary(mod_comp3) 

Multivariate Meta-Analysis Model (k = 64; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 46.7119  -93.4239  -81.4239  -68.6611  -79.8966   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0008  0.0283     10     no  Species_Latin 
sigma^2.2  0.0000  0.0000     25     no          Study 

outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)

            estim    sqrt  k.lvl  fixed                              level 
tau^2.1    0.0029  0.0540     32     no  Male sterlized/\nFemale sterlized 
tau^2.2    0.0256  0.1600     32     no        Male normal/\nFemale normal 
rho        0.0000                   yes                                    

Test for Residual Heterogeneity:
QE(df = 62) = 1423.9437, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 62) = 18.2633, p-val < .0001

Model Results:

                                            estimate      se    tval  df 
Comp_typeMale sterlized/\nFemale sterlized    0.0844  0.0170  4.9548  62 
Comp_typeMale normal/\nFemale normal          0.1547  0.0322  4.8113  62 
                                              pval   ci.lb   ci.ub      
Comp_typeMale sterlized/\nFemale sterlized  <.0001  0.0503  0.1184  *** 
Comp_typeMale normal/\nFemale normal        <.0001  0.0904  0.2190  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# with heteroscedasticity
mod_comp3b <-  rma.mv(abs_yi, V = abs_V_matrix_long, 
                      mod = ~ Comp_type, 
                      random = list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),
                      rho = 0, struct = "HCS",
                      data = sdat_long, test = "t")
summary(mod_comp3b)

Multivariate Meta-Analysis Model (k = 64; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 46.7119  -93.4239  -81.4239  -68.6611  -79.8966   

Variance Components:

            estim    sqrt  nlvls  fixed         factor 
sigma^2.1  0.0008  0.0283     10     no  Species_Latin 
sigma^2.2  0.0000  0.0000     25     no          Study 

outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)

            estim    sqrt  k.lvl  fixed                              level 
tau^2.1    0.0029  0.0540     32     no  Male sterlized/\nFemale sterlized 
tau^2.2    0.0256  0.1600     32     no        Male normal/\nFemale normal 
rho        0.0000                   yes                                    

Test for Residual Heterogeneity:
QE(df = 62) = 1423.9437, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 5.0020, p-val = 0.0289

Model Results:

                                      estimate      se    tval  df    pval 
intrcpt                                 0.0844  0.0170  4.9548  62  <.0001 
Comp_typeMale normal/\nFemale normal    0.0703  0.0314  2.2365  62  0.0289 
                                       ci.lb   ci.ub      
intrcpt                               0.0503  0.1184  *** 
Comp_typeMale normal/\nFemale normal  0.0075  0.1332    * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# this function does not work with heterogenious variance
#r2_ml(mod_comp3b) 

# without heteroscedasticity
mod_comp3c <-  rma.mv(abs_yi, V = abs_V_matrix_long, 
                      mod = ~ Comp_type, 
                      random = list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID),
                      #rho = 0, struct = "HCS",
                      data = sdat_long, test = "t")

# Comparing models with and without heteroscedasticity
AIC(mod_comp3, mod_comp3c)

           df       AIC
mod_comp3   6 -81.42389
mod_comp3c  5 491.98996

# visualizing results
f2 <- orchard_plot(mod_comp3b, mod = "Comp_type", 
                   xlab = "absolute log response ratio (lnRR)", 
             group = "Study", 
             angle = 45) +
  scale_fill_manual(values = c("#D55E00", "#009E73")) +
  scale_colour_manual(values = c("#D55E00", "#009E73")) 

f2

Figure: orchard plot (all combination)

patch3 <- f1 / f2 + plot_layout()

patch3 +  plot_annotation(tag_levels = "A")

13 PART II: HEALTHSPAN ANALYSIS ON LITERATURE DATA

14 Data preparation & processing

Healthspan data

# remove odl data
dat_full <- read.csv(here("data", "healthspan", "healthspan3.csv"), na = c("", "NA"))

dat_full %>% 
  mutate_if(is.character, as.factor) -> dat

# Effect_ID is the unique identifier for the effect
dat$Effect_ID <- factor(1:nrow(dat))

effect_type <- ifelse(str_detect(dat$measurement_parameter, "rop"), "proportion", "other")


kable(dat, "html") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", 
    height = "250px")

Study	order.checked	cohort	shared.control	Age.at.test	Age.at.test_months	Measure	Sub.measure	Checked	direction.of.improved.health	Measurement.type	Measure_exact.notes	Gonads_removed	Control_treatment	Sex	Species_Latin	Species	Strain	Mixedsexenvironment	Environment	Wild_or_semi_wild	Age_at_treatment	Age.at.treatment_weeks	Control_value	Experimental_value	Error_control_extracted	Error_experimental_extracted	measurement_parameter	measurement_unit	Error_unit	Error_control_SD	Error_experimental_SD	whole.IQR.control	whole.IQR.treatment	control.sample.size.range	experomental.sample.size.range	Coefficent_unit	Sample_size_control	Sample_size_experimental	Notes	Notes2	Notes3	Effect_ID
hewitson 2012	59	3	3	12 months	12	cardiac function	Cardiac function/pathology	checked	decreased	cardiac fibrosis	cardiac fibrosis	yes	Sham	Male	Mus musculus	Mice	RlnWT	no	Laboratory	No	4 weeks	4.00	0.9982000	0.9928000	0.062	0.04	mean	%	SEM	0.1518684	0.0979796	NA	NA	NA	NA	NA	6	6	fig2A	NA	NA	1
Morin-Grandmont 2024	87	51	51	12 months	12	cardiac function	Cardiac function/pathology	extracted	decreased	cardiac fibrosis	cardiac fibrosis	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	7 weeks	7.00	1.2855697	1.6530421	0.158024969	0.218865128	Mean	percentage	SEM	0.4327692	0.5993868	NA	NA	7 to 8	7 to 8	NA	8	8	Fig 4C	NA	NA	2
Morin-Grandmont 2024	88	52	52	24 months	24	cardiac function	Cardiac function/pathology	extracted	decreased	cardiac fibrosis	cardiac fibrosis	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	12 months	52.00	0.9014734	1.0728533	0.03182994	0.145292438	Mean	percentage	SEM	0.0871699	0.3978997	NA	NA	7 to 8	7 to 8	NA	8	8	Fig 4C	NA	NA	3
Moore 2022	170	69	69	14 months	14	cardiac function	Cardiac function/pathology	extracted	decreased	cardiac fibrosis	cardiac fibrosis	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	10 weeks	10.00	0.6498000	0.4276000	0.1524	0.0656	mean	secs	SEM	0.3407768	0.1466861	NA	NA	NA	NA	NA	5	5	Appendix A	NA	NA	4
Ayaz 2019	15	1	1	16-18 months	17	cardiac function	Cardiac function/pathology	Yes	Increased	E/A ratio	E/A ratio	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	1.5700000	1.6300000	0.06	0.05	mean	ratio	SE	0.2244994	0.2061553	NA	NA	NA	NA	NA	14	17	Fig8	NA	NA	5
banga 2021	24	19	19	18 months	18	cardiac function	Cardiac function/pathology	Yes	Increased	E/A ratio	E/A ratio	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	1.5900000	1.2600000	0.19	0.07	mean	ratio	SEM	0.6008328	0.2321637	NA	NA	NA	NA	NA	10	11	table 1	NA	NA	6
Walsh-Wilkinson 2022	172	70	70	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	E/A ratio	E/A ratio	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	1.6000000	1.5800000	0.03	0.04	mean	ratio	SEM	0.1200000	0.1131371	NA	NA	NA	NA	NA	16	8	Table S2 and 6	NA	NA	7
Walsh-Wilkinson 2022	171	71	71	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	E/A ratio	E/A ratio	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	1.6000000	1.5400000	0.03	0.05	mean	ratio	SEM	0.1161895	0.1414214	NA	NA	NA	NA	NA	15	8	Table S2 and 6	NA	NA	8
Ayaz 2019	16	1	1	16-18 months	17	cardiac function	Cardiac function/pathology	Yes	Increased	Ejection fraction	Ejection fraction	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	58.7900000	62.4800000	2.81	1.67	mean	%	SE	10.5140573	6.8855864	NA	NA	NA	NA	NA	14	17	Fig8	NA	NA	9
Fares 2013	39	2	2	24 months	24	cardiac function	Cardiac function/pathology	checked	Increased	Ejection fraction	Ejection fraction	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	60.4500000	60.4500000	2.69	2.32	mean	%	SE	6.5891274	5.6828162	NA	NA	NA	NA	NA	6	6	fig 2	NA	NA	10
banga 2021	17	19	19	18 months	18	cardiac function	Cardiac function/pathology	Yes	Increased	Ejection fraction	Ejection fraction	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	64.7500000	72.7100000	3.81	2.28	mean	%	SEM	12.0482779	7.5619045	NA	NA	NA	NA	NA	10	11	table 1	NA	NA	11
Morin-Grandmont 2024	86	52	52	24 months	24	cardiac function	Cardiac function/pathology	checked	Increased	Ejection fraction	Ejection fraction	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	12 months	52.00	58.5900000	56.3800000	2.34	1.86	mean	percentage	SEM	6.4083539	5.0938198	NA	NA	7 to 8	7 to 8	NA	8	8	Fig4B	NA	NA	12
Walsh-Wilkinson 2022	174	70	70	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	Ejection fraction	Ejection fraction	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	55.0000000	59.0000000	1.7	1.6	mean	percentage	SEM	6.8000000	4.5254834	NA	NA	NA	NA	NA	16	8	Table S2 and 6	NA	NA	13
Walsh-Wilkinson 2022	173	71	71	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	Ejection fraction	Ejection fraction	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	60.0000000	61.0000000	1.6	2.1	mean	percentage	SEM	6.1967734	5.9396970	NA	NA	NA	NA	NA	15	8	Table S2 and 6	NA	NA	14
Ayaz 2019	11	1	1	16-18 months	17	cardiac function	Cardiac function/pathology	Yes	Increased	fractional shortening	fractional shortening	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	26.7500000	29.2700000	2.36	1.39	mean	%	SE	11.0693812	7.4853791	NA	NA	NA	NA	NA	22	29	Fig8	NA	NA	15
Fares 2013	37	2	2	24 months	24	cardiac function	Cardiac function/pathology	checked	Increased	fractional shortening	LV fractional shortening	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	27.8300000	28.0500000	1.8	1.24	mean	%	SE	4.4090815	3.0373673	NA	NA	NA	NA	NA	6	6	fig 2	NA	NA	16
Walsh-Wilkinson 2022	176	70	70	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	fractional shortening	fractional shortening	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	33.2000000	32.2000000	1.2	1.6	mean	percentage	SEM	4.8000000	4.5254834	NA	NA	NA	NA	NA	16	8	Table S2 and 6	NA	NA	17
Walsh-Wilkinson 2022	175	71	71	12 months	12	cardiac function	Cardiac function/pathology	extracted	Increased	fractional shortening	fractional shortening	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	34.3000000	29.1000000	0.89	1.51	mean	percentage	SEM	3.4469552	4.2709250	NA	NA	NA	NA	NA	15	8	Table S2 and 6	NA	NA	18
Ayaz 2019	14	1	1	16-18 months	17	cardiac function	Cardiac function/pathology	Yes	decreased	Left Ventricular Isovolumic Relaxation Time	Left Ventricular Isovolumic Relaxation Time.	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	12.7400000	17.0000000	1.04	0.97	mean	ms	SE	4.8780324	5.2236099	NA	NA	NA	NA	NA	22	29	Fig8	NA	NA	19
banga 2021	23	19	19	18 months	18	cardiac function	Cardiac function/pathology	Yes	decreased	Left Ventricular Isovolumic Relaxation Time	Left Ventricular Isovolumic Relaxation Time.	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	10.4600000	12.5100000	0.83	0.45	mean	ms	SEM	2.6246905	1.4924812	NA	NA	NA	NA	NA	10	11	table 1	NA	NA	20
Moore 2022	169	69	69	14 months	14	cardiac function	Cardiac function/pathology	extracted	decreased	Left Ventricular Isovolumic Relaxation Time	Left Ventricular Isovolumic Relaxation Time.	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	10 weeks	10.00	0.0207000	0.0165000	0.0009	0.0008	mean	secs	SEM	0.0020125	0.0017889	NA	NA	NA	NA	NA	5	5	Appendix A	NA	NA	21
Walsh-Wilkinson 2022	182	70	70	12 months	12	cardiac function	Cardiac function/pathology	extracted	decreased	Left Ventricular Isovolumic Relaxation Time	Left Ventricular Isovolumic Relaxation Time.	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	15.3000000	18.0000000	0.47	0.7	mean	milliseconds	SEM	1.8800000	1.9798990	NA	NA	NA	NA	NA	16	8	Table S2 and 6	NA	NA	22
Walsh-Wilkinson 2022	181	71	71	12 months	12	cardiac function	Cardiac function/pathology	extracted	decreased	Left Ventricular Isovolumic Relaxation Time	Left Ventricular Isovolumic Relaxation Time.	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	15.8000000	15.8000000	0.39	0.81	mean	milliseconds	SEM	1.5104635	2.2910260	NA	NA	NA	NA	NA	15	8	Table S2 and 6	NA	NA	23
Ayaz 2019	10	1	1	16-18 months	17	cardiac function	Cardiac size	Yes	decreased	Cardiomyocyte size	Myoctye area	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	4 weeks	4.00	3738.0000000	3266.0000000	186	207	mean	um squared	SE	1302.0000000	1325.4467170	NA	NA	NA	NA	NA	49	41	table 1	These sample sizes look too big. Are these multiple readings from the same animals?	NA	24
Fares 2013	41	2	2	24 months	24	cardiac function	Cardiac size	extracted	decreased	Cardiomyocyte size	Myoctye fibre size	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	35.4000000	29.8000000	1.6	1.3	mean	pL	SEM	6.1967734	5.2000000	NA	NA	NA	NA	NA	15	16	table 1	NA	NA	25
banga 2021	25	19	19	20 -26 months	23	cardiac function	Cardiac size	Yes	decreased	Cardiomyocyte size	myocyte fiber width	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	28.3700000	34.3400000	1.29	1.21	mean	um	SEM	8.6535831	7.9345006	NA	NA	NA	NA	NA	45	43	fig 6	These sample sizes look too big. Are these multiple readings from the same animals?	NA	26
Morin-Grandmont 2024	90	51	51	24 months	24	cardiac function	Cardiac size	extracted	decreased	Cardiomyocyte size	Cardiomyocyte CSA	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	12 months	52.00	493.6313582	457.5598274	32.56423702	64.84111416	Mean	um2	SEM	89.1808359	177.5747044	NA	NA	7 to 8	7 to 8	NA	8	8	Fig2D	NA	NA	27
Morin-Grandmont 2024	89	51	51	12 months	12	cardiac function	Cardiac size	extracted	decreased	Cardiomyocyte size	Cardiomyocyte CSA	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	7 weeks	7.00	352.1735711	352.9776383	38.02307289	63.28756375	Mean	um2	SEM	104.1304736	173.3201314	NA	NA	7 to 8	7 to 8	NA	8	8	Fig3A	NA	NA	28
Ayaz 2019	9	1	1	16-18 months	17	cardiac function	Cardiac size	Yes	decreased	Heart size	heart weight/tibia weight	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	11.2000000	9.6000000	0.9	1	mean	mg/mm	SE	2.0124612	2.2360680	NA	NA	NA	NA	NA	5	5	table 1	Sample size changed from 9 to 5	NA	29
banga 2021	26	19	19	22-24 months	23	cardiac function	Cardiac size	Yes	decreased	Heart size	heart size rel to body weight	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	4.0600000	4.3900000	0.38	0.34	mean	mg/g	SEM	1.1078809	0.9912618	NA	NA	6 to 11	6 to 11	NA	9	9	fig 6	NA	NA	30
Pijacka 2015	100	24	24	12 months	12	cardiac function	Cardiac size	checked	decreased	Heart size	heart siz/body weight	yes	Sham	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	10 weeks	10.00	0.3200000	0.2600000	0.02	0.02	mean	%	SEM	0.0565685	0.0565685	NA	NA	NA	NA	NA	8	8	Table 1	NA	NA	31
Pijacka 2015	101	25	25	18 months	18	cardiac function	Cardiac size	checked	decreased	Heart size	heart siz/body weight	yes	Sham	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	10 weeks	10.00	0.3000000	0.2500000	0.01	0.01	mean	%	SEM	0.0282843	0.0282843	NA	NA	NA	NA	NA	8	8	Table 1	NA	NA	32
Morin-Grandmont 2024	82	51	51	12 months	12	cardiac function	Cardiac size	checked	decreased	Heart size	heart weight/tibia weight	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	7 weeks	7.00	5.4452926	5.5216285	0.229007634	0.190839695	Mean	mg/mm	SEM	0.6271632	0.5397762	NA	NA	NA	NA	NA	8	8	Fig2D	NA	NA	33
Morin-Grandmont 2024	84	52	52	24 months	24	cardiac function	Cardiac size	checked	decreased	Heart size	heart weight/tibia weight	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	12 months	52.00	6.6157761	5.7633588	0.114503817	0.127226463	Mean	mg/mm	SEM	0.3238657	0.3598508	NA	NA	NA	NA	NA	8	8	Fig2D	NA	NA	34
banga 2023	163	66	66	25 months	25	cardiac function	Cardiac size	extracted	decreased	Heart size	heart weight/tibia weight	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	9.0400000	8.0000000	0.6	0.34	mean	mg/mm	SE	1.5874508	0.8995554	NA	NA	NA	NA	NA	7	7	table 2	NA	NA	35
Walsh-Wilkinson 2022	178	70	70	12 months	12	cardiac function	Cardiac size	extracted	decreased	Heart size	heart weight/tibia weight	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	4.6000000	4.5000000	0.03	0.26	mean	mg/g	SEM	0.1200000	0.7353911	NA	NA	NA	NA	NA	16	8	Table 2 and S6	NA	NA	36
Walsh-Wilkinson 2022	177	71	71	12 months	12	cardiac function	Cardiac size	extracted	decreased	Heart size	heart weight/tibia weight	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	4.3000000	3.3000000	0.15	0.28	mean	mg/g	SEM	0.5809475	0.7919596	NA	NA	NA	NA	NA	15	8	Table 2 and S6	NA	NA	37
Ayaz 2019	13	1	1	16-18 months	17	cardiac function	Cardiac size	Yes	decreased	left ventricle size	IVPWd	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	0.0659339	0.0647975	0.004685621	0.005139274	mean	cm	SE	0.0219775	0.0276758	NA	NA	NA	NA	NA	22	29	Fig8	NA	NA	38
Ayaz 2019	12	1	1	16-18 months	17	cardiac function	Cardiac size	Yes	decreased	left ventricle size	LV mass	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4weeks	4.00	0.0915000	0.0750000	0.005625	0.0045	mean	g	SE	0.0263836	0.0242332	NA	NA	NA	NA	NA	22	29	Fig8	NA	NA	39
Fares 2013	38	2	2	24 months	24	cardiac function	Cardiac size	extracted	decreased	left ventricle size	LVPWd	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	0.8022275	0.7335076	0.080777357	0.01362378	mean	mm	SE	0.1978633	0.0333713	NA	NA	NA	NA	NA	6	6	fig 2	just extracted LVPWd but other heart measurement values are also included	NA	40
Fares 2013	40	2	2	24 months	24	cardiac function	Cardiac size	checked	decreased	left ventricle size	ventricle weight/body weight	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	4 weeks	4.00	6.3000000	5.2000000	0.4	0.3	mean	mg/g	SEM	0.8944272	0.6000000	NA	NA	NA	NA	NA	5	4	table 1	NA	NA	41
hewitson 2012	58	3	3	12 months	12	cardiac function	Cardiac size	checked	decreased	left ventricle size	LV/BW	yes	Sham	Male	Mus musculus	Mice	RlnWT	no	Laboratory	No	4 weeks	4.00	3.6000000	3.5000000	0.1	0.3	mean	mg/g	SEM	0.2449490	0.7348469	NA	NA	NA	NA	NA	6	6	table 1	NA	NA	42
banga 2021	18	19	19	18 months	18	cardiac function	Cardiac size	Yes	decreased	left ventricle size	left ventricle mass	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	144.0200000	118.2000000	8.69	11.87	mean	mg	SEM	27.4801929	39.3683363	NA	NA	NA	NA	NA	10	11	table 1	NA	NA	43
banga 2021	19	19	19	18 months	18	cardiac function	Cardiac size	Yes	decreased	left ventricle size	LVAWd	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	1.1700000	1.0300000	0.06	0.08	mean	mm	SEM	0.1897367	0.2653300	NA	NA	NA	NA	NA	10	11	table 1	Should we just be using the diastole values here? That is the more important one (i.e. LVPWd)	NA	44
banga 2021	20	19	19	18 months	18	cardiac function	Cardiac size	Yes	decreased	left ventricle size	LVAWs	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	1.6400000	1.4900000	0.07	0.1	mean	mm	SEM	0.2213594	0.3316625	NA	NA	NA	NA	NA	10	11	table 1	Should we just be using the diastole values here? That is the more important one (i.e. LVPWd)	NA	45
banga 2021	21	19	19	18 months	18	cardiac function	Cardiac size	Yes	decreased	left ventricle size	LVPWd	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	0.9200000	0.8400000	0.06	0.05	mean	mm	SEM	0.1897367	0.1658312	NA	NA	NA	NA	NA	10	11	table 1	Should we just be using the diastole values here? That is the more important one (i.e. LVPWd)	NA	46
banga 2021	22	19	19	18 months	18	cardiac function	Cardiac size	Yes	decreased	left ventricle size	LVPWs	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	1 month	4.00	1.3500000	1.4300000	0.08	0.07	mean	mm	SEM	0.2529822	0.2321637	NA	NA	NA	NA	NA	10	11	table 1	Should we just be using the diastole values here? That is the more important one (i.e. LVPWd)	NA	47
Morin-Grandmont 2024	83	51	51	12 months	12	cardiac function	Cardiac size	checked	decreased	left ventricle size	posterior LV wall thickness + interventricular septal wall thickness	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	7 weeks	7.00	1.6000000	1.6800000	0.6	0.05	Mean	mm	SEM	1.6970563	0.1414214	NA	NA	NA	NA	NA	8	8	Fig3A	NA	NA	48
Morin-Grandmont 2024	85	52	52	24 months	24	cardiac function	Cardiac size	checked	decreased	left ventricle size	posterior LV wall thickness + interventricular septal wall thickness	yes	intact	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	12 months	52.00	1.8300000	1.6100000	0.07	0.04	Mean	mm	SEM	0.1979899	0.1131371	NA	NA	NA	NA	NA	8	8	Fig3A	NA	NA	49
Walsh-Wilkinson 2022	180	70	70	12 months	12	cardiac function	Cardiac size	extracted	decreased	left ventricle size	PW: diastolic posterior wall thickness	yes	Sham	Male	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	0.9300000	0.8100000	0.02	0.012	mean	mm	SEM	0.0800000	0.0339411	NA	NA	NA	NA	NA	16	8	Table S2 and 6	NA	NA	50
Walsh-Wilkinson 2022	179	71	71	12 months	12	cardiac function	Cardiac size	extracted	decreased	left ventricle size	PW: diastolic posterior wall thickness	yes	Sham	Female	Mus musculus	Mice	C57BL6	no	NA	NA	6 weeks	6.00	0.8400000	0.9000000	0.01	0.018	mean	mm	SEM	0.0387298	0.0509117	NA	NA	NA	NA	NA	15	8	Table S2 and 6	NA	NA	51
picazo 2016	96	21	21	13 months	13	Cognition	Cognition	extracted	Increased	Autoshaping learning test	number of conditioned responses	yes	intact	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	0.4370445	0.1030803	0.109442319	0.109683914	mean	number	SEM	0.2447205	0.2452607	NA	NA	NA	NA	NA	5	5	fig1A	NA	NA	52
picazo 2016	97	21	21	18 months	18	Cognition	Cognition	extracted	Increased	Autoshaping learning test	number of conditioned responses	yes	intact	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	0.0968794	0.0927723	0.114032615	0.120958325	mean	number	SEM	0.2549847	0.2704710	NA	NA	NA	NA	NA	5	5	fig1A	NA	NA	53
Jiang et al Biorvx 2023	141	60	60	12.5 months	12.5	Cognition	Cognition	extracted	decreased	Barnes maze test	Barnes Maze test_time spent with incorrect hole	Yes	Sham	Male	Mus musculus	Mice	C57BL6	no	Laboratory	No	8 weeks	8.00	57.7310000	17.7235000	13.52813	5.63127	mean	sec	SEM	48.7763664	13.7937381	NA	NA	NA	NA	NA	13	6	fig1F	12-13 months oldStated that hair greying delayed and also there is rectal temperator data	NA	54
Jiang et al Biorvx 2023	140	60	60	12.5 months	12.5	Cognition	Cognition	extracted	decreased	Barnes maze test	Barnes Maze test_time to find correct hole	Yes	Sham	Male	Mus musculus	Mice	C57BL6	no	Laboratory	No	8 weeks	8.00	162.5326000	69.9328000	25.45105	13.07186	mean	sec	SEM	80.4832868	32.0193870	NA	NA	NA	NA	NA	10	6	fig 1E	12-13 months oldStated that hair greying delayed and also there is rectal temperator data	NA	55
Leffa 2013	76	12	12	18 months	18	Cognition	Cognition	extracted	Increased	inhibitory avoidance test	inhibitory avoidance test	yes	Sham	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	4.1700000	9.3500000	0.59(lower)6.31 (upper)	3.72(lower)25.05(upper)	median	latency to step	interquartile interval	4.3630816	16.2700229	5.720	21.33	NA	NA	NA	12	12	Fig2	NA	NA	56
Hou 2013	63	5	5	12 months	12	Cognition	Cognition	checked	decreased	Morris water maze	morris water maze latency to find platform in probe test	yes	Sham	Female	Mus musculus	Mice	ICR	no	Laboratory	No	8 weeks	8.00	22.1600000	36.5000000	6.89	4.5	mean	s	SD	6.8900000	4.5000000	NA	NA	NA	NA	NA	10	10	Fig 1	NA	NA	57
Hou 2013	64	5	5	12 months	12	Cognition	Cognition	checked	Increased	Morris water maze	morris water mazeplatform crossings	yes	Sham	Female	Mus musculus	Mice	ICR	no	Laboratory	No	8 weeks	8.00	2.8900000	1.8500000	1.215	1.39	mean	n	SD	1.2150000	1.3900000	NA	NA	NA	NA	NA	10	10	fig 1	NA	NA	58
Hou 2013	65	6	6	12 months	12	Cognition	Cognition	checked	decreased	Morris water maze	morris water maze latency to find platform in probe test	yes	Sham	Male	Mus musculus	Mice	ICR	no	Laboratory	No	8 weeks	8.00	27.6600000	34.3200000	10.16	5.95	mean	s	SD	10.1600000	5.9500000	NA	NA	NA	NA	NA	10	10	Fig 1	NA	NA	59
Hou 2013	66	6	6	12 months	12	Cognition	Cognition	checked	Increased	Morris water maze	morris water mazeplatform crossings	yes	Sham	Male	Mus musculus	Mice	ICR	no	Laboratory	No	8 weeks	8.00	2.4240000	2.6500000	1.54	1.59	mean	n	SD	1.5400000	1.5900000	NA	NA	NA	NA	NA	10	10	Fig 1	NA	NA	60
Koebele 2023	71	20	20	early middle age 12 months	12	Cognition	Cognition	checked	Increased	Morris water maze	moris maze: probe trial NE (correct) quadrant	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	0.4700000	0.4900000	0.059	0.046	mean	proportion	SE	0.1865744	0.1380000	NA	NA	NA	NA	NA	10	9	fig 4D	NA	NA	61
Koebele 2023	72	20	20	late middle age at 17 months	17	Cognition	Cognition	checked	Increased	Morris water maze	moris maze: probe trial NE (correct) quadrant	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	0.3900000	0.3700000	0.04	0.051	mean	proportion	SE	0.1200000	0.1349333	NA	NA	NA	NA	NA	9	7	fig4F	NA	NA	62
galea 2018	42	22	22	14 months	14	Cognition	Cognition	checked	Increased	Morris water maze	Morris water maze (percentage of time in quadrant in probe trial)	yes	Sham	Female	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	8 months	35.00	29.4000000	31.7000000	2.5	2.2	mean	%	SEM	7.9056942	6.9570109	NA	NA	NA	NA	NA	10	10	table 3	nulliparous	Data is also presented for a second test, reversal learning. Have not included this but could come under other memory tests? Stated that it is a test of cognitive flexibility	63
galea 2018	43	23	23	14 months	14	Cognition	Cognition	checked	Increased	Morris water maze	Morris water maze (percentage of time in quadrant in probe trial)	yes	Sham	Female	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	8 months	35.00	28.5000000	34.3000000	4.6	2.9	mean	%	SEM	13.0107648	9.6182119	NA	NA	NA	NA	NA	8	11	table 3	primiparous	Data is also presented for a second test, reversal learning. Have not included this but could come under other memory tests? Stated that it is a test of cognitive flexibility	64
Itoh et al 2023	137	57	57	12-14 months	13	Cognition	Cognition	extracted	Increased	Morris water maze	Morris water maze probe trial time in correct quadrant	Yes	unclear	Male	Mus musculus	Mice	C57BL6	no	Laboratory	No	2 months old	8.00	37.5000000	39.5000000	36.101 (lower) 41.134 (upper)	36.38(lower) 41.28 (upper)	median	percentage in correct zone	IQR	3.8244681	3.7234043	5.033	4.90	NA	NA	NA	14	7	Fg 2	NA	NA	65
Itoh et al 2023	138	58	58	12-14 months	13	Cognition	Cognition	extracted	Increased	Morris water maze	Morris water maze probe trial time in correct quadrant	Yes	unclear	Female	Mus musculus	Mice	C57BL6	no	NA	NA	2 months old	8.00	34.9191770	25.8702356	33.81(lower) 44.08 (upper)	20.34 (lower)31.18(upper)	median	percentage in correct zone	IQR	7.7218045	8.1503759	10.270	10.84	NA	NA	NA	30	8	Fig 2	NA	NA	66
Borbelyova et al 2016	134	77	77	30 months	30	Cognition	Cognition	extracted	Increased	novel object recognition	Novel object recognition test	Yes	unclear	Male	Ratus ratus	Rat	wistar albino	no	Laboratory	No	Day 47	6.50	4.1732271	12.1028876	1.483910276	3.075325883	mean	difference exploring novel object	SEM	5.7471598	11.5068158	NA	NA	NA	NA	NA	15	14	Fig3C	NA	NA	67
Leffa 2013	75	12	12	18 months	18	Cognition	Cognition	checked	Increased	novel object recognition	novel object recognition	yes	Sham	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	0.6780000	0.6660000	0.068	0.036	mean	recognition index	SE	0.2355589	0.1247077	NA	NA	NA	NA	NA	12	12	Fig1	NA	NA	68
Wang 2021	121	53	53	14-15months	14.5	Cognition	Cognition	checked	Increased	novel object recognition	novel object recognition	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	8months	35.00	0.2800000	0.3900000	0.02	0.029	mean	index	SEM	0.0565685	0.0820244	NA	NA	NA	NA	NA	8	8	fig5i	NA	NA	69
Wang 2021	126	54	54	18 month	18	Cognition	Cognition	checked	Increased	novel object recognition	novel recognition open field	Yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	8 months	35.00	0.1700000	0.1900000	0.03	0.03	mean	index	S.E.M	0.0848528	0.0848528	NA	NA	NA	NA	NA	8	8	Fig S3E	NA	NA	70
Heikkinen 2004	52	10	10	24 months	24	Cognition	Cognition	checked	decreased	RAM memory test	RAM memory test: reference memory errors	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	5 months	22.00	111.9100000	115.7700000	6.53	3.85	mean	n	SEM	17.2767561	12.7690054	NA	NA	NA	NA	NA	7	11	fig 1 & n in text	NA	NA	71
Heikkinen 2004	55	10	10	24 months	24	Cognition	Cognition	checked	decreased	RAM memory test	RAM memory test: reference memory errors	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	NA	NA	5 months	22.00	111.3700000	115.0300000	7.32	5.23	mean	n	SEM	19.3668996	17.3459476	NA	NA	NA	NA	NA	7	11	fig2	NA	NA	72
Heikkinen 2004	53	10	10	24 months	24	Cognition	Cognition	checked	decreased	RAM memory test	RAM memory test: rworking memory errors	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	5 months	22.00	45.4300000	32.9800000	12.98	5.82	mean	n	SEM	34.3418520	19.3027563	NA	NA	NA	NA	NA	7	11	fig 1	NA	NA	73
Heikkinen 2004	56	10	10	24 months	24	Cognition	Cognition	checked	decreased	RAM memory test	RAM memory test: rworking memory errors	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	NA	NA	5 months	22.00	46.3400000	33.9100000	12.34	5.7	mean	n	SEM	32.6485712	18.9047613	NA	NA	NA	NA	NA	7	11	fig2	NA	NA	74
Heikkinen 2004	54	10	10	24 months	24	Cognition	Cognition	checked	Increased	T maze test	T maze total correct choices	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	5 months	22.00	35.9300000	29.3750000	0.94	1.87	mean	n	SEM	2.4870062	6.2020884	NA	NA	NA	NA	NA	7	11	fig 1	NA	NA	75
Koebele 2023	69	20	20	early middle age 12 months	12	Cognition	Cognition	checked	decreased	water radial arm maze test	water radial arm maze: delayed memory retention number of errors	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	1.9100000	1.5400000	0.525	0.395	mean	number	SE	1.5750000	1.1850000	NA	NA	NA	NA	NA	9	9	fig2H	Data is also presented for a	NA	76
Koebele 2023	67	20	20	early middle age 12 months	12	Cognition	Cognition	checked	decreased	water radial arm maze test	water radial arm maze: WMI errors maximum working memory load	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	1.6060000	1.3240000	0.28	0.2	mean	number	SE	0.8854377	0.6000000	NA	NA	NA	NA	NA	10	9	fig2H	Data is also presented for a	NA	77
Koebele 2023	68	20	20	late middle age at 17 months	17	Cognition	Cognition	checked	decreased	water radial arm maze test	water radial arm maze: WMI errors maximum working memory load	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	1.5300000	1.2800000	0.22	0.19	mean	number	SE	0.6600000	0.5026927	NA	NA	NA	NA	NA	9	7	fig2J	NA	NA	78
Koebele 2023	70	20	20	late middle age at 17 months	17	Cognition	Cognition	checked	decreased	water radial arm maze test	water radial arm maze:delayed memory retention number of errors	yes	Sham	Female	Ratus ratus	Rat	Fischer 344 CDF	no	Laboratory	No	5 months	22.00	2.0900000	1.3500000	0.347	0.66	mean	number	SE	1.0410000	1.7461959	NA	NA	NA	NA	NA	9	7	fig2J	NA	NA	79
Itoh et al 2023	139	59	59	12-14 months	13	Cognition	Cognition	extracted	Increased	Y maze testing	Y maze testing	Yes	unclear	Female	Mus musculus	Mice	C57BL6	no	NA	NA	2 months old	8.00	57.1200000	46.1700000	53.06(lower)72.71 (upper)	39.45(lower)50.19(upper_	median	percentage spontaneous alteration	IQR	14.9657273	8.1797411	19.650	10.74	NA	NA	NA	13	16	Fig 2	NA	NA	80
Zakeri et al 2019	148	63	63	23 months	23	Cognition	Cognition	extracted	Increased	Y maze testing	Y maze testing	Yes	Sham	Female	Mus musculus	Mice	NMRI	no	Laboratory	No	10 months	43.00	68.5712498	23.4193649	5.259502581	8.499296105	mean	percentage spontaneous alteration	S.E.M	11.7606053	19.0050039	NA	NA	NA	NA	NA	5	5	Fig 4	NA	NA	81
Zakeri et al 2019	149	64	63	23 months	23	Cognition	Cognition	extracted	Increased	Y maze testing	Y maze testing	Yes	intact	Female	Mus musculus	Mice	NMRI	no	Laboratory	No	10 months	43.00	63.7760050	23.4193649	13.33583607	8.499296105	mean	percentage spontaneous alteration	S.E.M	26.6716721	19.0050039	NA	NA	NA	NA	NA	4	5	Fig 4	NA	NA	82
Heinze-Milne 2021	57	50	50	18 months	18	Frailty assessment	Frailty	extracted	decreased	Frailty score	Frailty score	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	4 weeks	4.00	0.2239073	0.1773100	0.036645285	0.04081906	mean	frailty score	95% confidence interval (whole interval)	0.0093483	0.0104130	NA	NA	NA	NA	NA	10	11	fig 2	extracted mean and CI from figure	sham points: .287, .255, .246, .206,.19; points OX .22, .19, .17, .15, .12, .05, .18, .2, .23 but points # don't tally to n=10 or 11	83
de la Fuente 2004	30	34	34	18 months	18	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; IL2 axillary leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	435.4564033	52.2479564	95.74250681	12.07425068	mean	pg/ml	Sd	95.7425068	12.0742507	NA	NA	NA	NA	NA	6	6	Fig 7b	IL2 production mostly from T cellsin axi and spleen ChatGpt	why does it say chat gtp in adjacent cell	84
de la Fuente 2004	31	34	34	22 months	22	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; IL2 axillary leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	213.7772480	51.7540872	55.05790191	20.45299728	mean	pg/ml	Sd	55.0579019	20.4529973	NA	NA	NA	NA	NA	6	4	Fig 7b	IL2 production mostly from T cellsin axi and spleen ChatGpt	why does it say chat gtp in adjacent cell	85
de la Fuente 2004	32	34	34	24 months	24	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; IL2 spleen leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	367.5408719	139.8160763	76.72002725	44.85694823	mean	pg/ml	Sd	76.7200272	44.8569482	NA	NA	NA	NA	NA	6	5	Fig 7b	NA	NA	86
de la Fuente 2004	27	34	34	18 months	18	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; Stimulation axillary leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	446.5245870	180.4184182	215.661355	69.21497703	mean	index	Sd	215.6613550	69.2149770	NA	NA	NA	NA	NA	6	6	Fig 7a	Stim on ConA specific to Tcells in axi and spleen ChatGpt	NA	87
de la Fuente 2004	28	34	34	22 months	22	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; Stimulation axillary leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	141.7538371	138.0877896	54.25750318	92.28663603	mean	index	Sd	54.2575032	92.2866360	NA	NA	NA	NA	NA	6	4	Fig 7a	Stim on ConA specific to Tcells in axi and spleen ChatGpt	NA	88
de la Fuente 2004	29	34	34	24 months	24	Immune Function	Immune Function	reextracted	Increased	T cell function test	T cells function test in vitro; Stimulation axillary leu w/ConA	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	12 months	52.00	584.5146153	438.9480888	182.2270017	185.4531235	mean	index	Sd	182.2270017	185.4531235	NA	NA	NA	NA	NA	6	5	Fig 7a	Stim on ConA specific to Tcells in axi and spleen ChatGpt	NA	89
Apelo 2020	2	7	7	12 months	12	activity, energetics and metabolism	Metabolism	Yes	Increased	Energy expenditure	energy expenditure dark	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	19.5500000	15.1600000	1.071	0.94	mean	kcal/kg/hr	SEM	3.3867994	2.9725410	NA	NA	NA	NA	NA	10	10	fig 4	Just dark period data used as this is when animals are active	Mice WT of Rictor flox mice	90
Apelo 2020	4	8	8	12 months	12	activity, energetics and metabolism	Metabolism	Yes	Increased	Energy expenditure	energy expenditure dark	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	14.7600000	14.8900000	0.66	0.8	mean	kcal/kg/hr	SEM	2.1889724	2.5298221	NA	NA	NA	NA	NA	11	10	fig 4	Just dark period data used as this is when animals are active	Mice WT of Rictor flox mice	91
Garratt et al 2017	142	30	30	22 months	22	activity, energetics and metabolism	Metabolism	extracted	decreased	Glucose tolerance	glucose tolerance area under the cruve	Yes	Sham	Male	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	9337.9349020	9880.6440420	981.8162399	676.3504312	mean	abituary units	SEM	2776.9956840	2029.0512940	NA	NA	NA	NA	NA	8	9	Fig 5	NA	NA	92
Garratt et al 2017	143	31	31	22 months	22	activity, energetics and metabolism	Metabolism	extracted	decreased	Glucose tolerance	glucose tolerance area under the cruve	Yes	Sham	Female	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	7309.0000000	8483.0000000	281.1	557.9	mean	abituary units	SEM	843.3000000	1932.6222910	NA	NA	NA	NA	NA	9	12	Fig 5	NA	NA	93
Borbelyova et al 2017	144	61	61	18 months	18	activity, energetics and metabolism	Metabolism	extracted	decreased	Glucose tolerance	glucose tolerance area under the cruve	Yes	Sham	Male	Ratus ratus	Rat	Lewis	no	NA	NA	30 days	4.00	203.4000000	216.2000000	212.15	226.78	mean	abituary units	SEM	1081.7569900	962.1460550	NA	NA	NA	NA	NA	26	18	Fig 3A	NA	NA	94
Apelo 2020	5	7	7	12 months	12	activity, energetics and metabolism	Metabolism	extracted	decreased	Insulin sensitivity	Insulin tolerance test	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	5102.0000000	4895.0000000	206	187	mean	abitrary units (AUC)	SEM	600.5880452	545.1940022	NA	NA	8 to 9	8 to 9	NA	9	9	fig 5 supplement 1	NA	Mice WT of Rictor flox mice	95
Apelo 2020	6	8	8	13 months	13	activity, energetics and metabolism	Metabolism	extracted	decreased	Insulin sensitivity	Insulin tolerance test	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	5567.0000000	5380.0000000	192	378	mean	abitrary units (AUC)	SEM	559.7713819	1102.0499080	NA	NA	7 to 10	7 to 10	NA	9	9	fig 5 supplement 1	NA	Mice WT of Rictor flox mice	96
Garratt 2018	44	30	30	22-24 months	23	skeletal Muscle function	Muscle size	checked	Increased	Quadriceps size	quadriceps size	yes	Sham	Male	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	0.1230000	0.1460000	0.005	0.007	mean	g	SEM	0.0187083	0.0252389	NA	NA	NA	NA	NA	14	13	fig5	got exact sample sizes from data files	NA	97
Garratt 2018	47	31	31	22-24 months	23	skeletal Muscle function	Muscle size	checked	Increased	Quadriceps size	quadriceps size	yes	Sham	Female	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	0.1180000	0.1110000	0.004	0.006	mean	g	SEM	0.0132665	0.0216333	NA	NA	NA	NA	NA	11	13	fig5	got exact sample sizes from data files	NA	98
Garratt 2018	45	30	30	22-24 months	23	skeletal Muscle function	Muscle size	checked	Increased	Skeletal muscle fiber size	fiber CSA	yes	Sham	Male	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	2153.0000000	2461.0000000	70	108	mean	um2	SEM	252.3885893	418.2822014	NA	NA	NA	NA	NA	13	15	fig5	got exact sample sizes from data files	NA	99
Garratt 2018	48	31	31	22-24 months	23	skeletal Muscle function	Muscle size	checked	Increased	Skeletal muscle fiber size	fiber CSA	yes	Sham	Female	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	2130.0000000	2029.0000000	168	93	mean	um2	SEM	531.2626469	308.4461055	NA	NA	NA	NA	NA	10	11	fig5	got exact sample sizes from data files	NA	100
Mason 2011	80	4	4	end of life	death	Pathology at death	NonTumor_pathology	checked	decreased	Cardiac pathology	atrial thrombosis at death	yes	Sham	Female	Mus musculus	Mice	CBA/J	no	Laboratory	No	21 days	3.00	0.3800000	0.4400000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	34	25	table 1	NA	NA	101
Mason 2011	79	4	4	end of life	death	Pathology at death	NonTumor_pathology	checked	decreased	Cardiac pathology	cardiac fibrosis at death	yes	Sham	Female	Mus musculus	Mice	CBA/J	no	Laboratory	No	21 days	3.00	0.4100000	0.8400000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	34	25	table 1	NA	NA	102
Mason 2011	78	4	4	end of life	death	Pathology at death	NonTumor_pathology	checked	decreased	Cardiac pathology	cardiomyopathy at death	yes	Sham	Female	Mus musculus	Mice	CBA/J	no	Laboratory	No	21 days	3.00	0.7300000	0.5800000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	30	24	table 1	NA	NA	103
Holand 1977	196	76	76	At death	death	Pathology at death	NonTumor_pathology	extracted	decreased	Cardiac pathology	atrial thrombosis	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.2290000	0.1280000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	104
Holand 1977	198	76	76	At death	death	Pathology at death	NonTumor_pathology	extracted	decreased	Cardiac pathology	Valvular endocarditis (heart inflammation disorder)	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.1100000	0.0370000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	Metritis and otitis media are also listed in paper but not in search protocol (V similar between groups)	105
Drori and Folman 1976	167	68	68	End of life	death	Pathology at death	NonTumor_pathology	extracted	decreased	Kidney pathology	Proportion with Nephritis	yes	intact	Male	Ratus ratus	Rat	Albino	no	NA	NA	41 days	6.00	0.2500000	0.2390000	NA	NA	proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	46	Table 3	Other pathologies (pneumonia and enteritis) are listed but not included in healthspan search protocol	NA	106
Holand 1977	195	76	76	At death	death	Pathology at death	NonTumor_pathology	extracted	decreased	Kidney pathology	Severe glomerulosclerosis	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.4500000	0.6700000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	107
Holand 1977	197	76	76	At death	death	Pathology at death	NonTumor_pathology	extracted	decreased	Polyarteritis	Polyarteritis	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.0280000	0.0680000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	108
Drori and Folman 1976	166	68	68	End of life	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Proportion with tumors	Proportion with tumors (eccept hypophyseal adenoma)	yes	intact	Male	Ratus ratus	Rat	Albino	no	NA	NA	41 days	6.00	0.1040000	0.0870000	NA	NA	proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	46	Table 3	Other pathologies (pneumonia and enteritis) are listed but not included in healthspan search protocol	NA	109
hewitson 2012	60	3	3	12 months	12	Pathology cross sectional	NonTumor_pathology	checked	decreased	Kidney pathology	kidney fibrosis (relative collagen conc)	yes	Sham	Male	Mus musculus	Mice	RlnWT	no	Laboratory	No	4 weeks	4.00	0.9990000	1.0000000	0.041	0.024	mean	%	SEM	0.1004291	0.0587878	NA	NA	NA	NA	NA	6	6	fig2C	NA	NA	110
Urbieta 2012	117	9	9	18-22 months	20	pathology cross sectional	NonTumor_pathology	checked	decreased	Kidney pathology	kidney fibrosis	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	6 months	26.00	2.4700000	2.7500000	0.4	0.3	mean	% cortical area	SE	1.3266499	0.9949874	NA	NA	NA	NA	NA	11	11	fig4	NA	NA	111
Urbieta 2012	115	9	9	18-22 months	20	pathology cross sectional	NonTumor_pathology	checked	Increased	Kidney pathology	presence of kidney lesions (higher score better)	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	6 months	26.00	6.9000000	6.5000000	0.6	0.6	mean	#/field	SE	1.9899749	1.9899749	NA	NA	NA	NA	NA	11	11	text	NA	NA	112
Urbieta 2012	116	9	9	18-22 months	20	pathology cross sectional	NonTumor_pathology	checked	decreased	Kidney pathology	presence of kidney lesions segmental glomerulosclerosis	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	6 months	26.00	2.0000000	2.2000000	0.4	0.5	mean	%glomerular score	SE	1.3266499	1.6583124	NA	NA	NA	NA	NA	11	11	text	NA	NA	113
Reckelhoff 1993	109	13	13	18-20 months	19	Pathology cross sectional	NonTumor_pathology	checked	Increased	Kidney pathology	presence of kidney lesions (higher score better)	yes	intact	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	10 weeks	10.00	91.0000000	92.0000000	2	1	mean	proportion of normal glomerular	SEM (assumed)	4.4721360	2.0000000	NA	NA	NA	NA	NA	5	4	text	Information is provided on severity of damage also, but not sure how to extract this and the SE . Stated that it is more severe in males	NA	114
Reckelhoff 1993	110	14	14	18-20 months	19	Pathology cross sectional	NonTumor_pathology	checked	Increased	Kidney pathology	presence of kidney lesions	yes	intact	Male	Ratus ratus	Rat	wistar	no	Laboratory	No	10 weeks	10.00	83.0000000	92.0000000	2	2	mean	proportion of normal glomerular	SEM (assumed)	5.2915026	3.4641016	NA	NA	NA	NA	NA	7	3	text	NA	NA	115
Herrara et al 2020	128	30	30	25 months	25	Pathology cross sectional	NonTumor_pathology	extracted	decreased	Liver pathology	Hepatic lipidosis	Yes	Sham	Male	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	2.2857000	1.5385000	0.4738	0.4178	mean	score	SEM	1.7728105	1.5063966	NA	NA	NA	NA	NA	14	13	unpublished data	Data that was unpublished from Herrara et al. Pathology method included in paper for sham animals	NA	116
Herrara et al 2020	129	30	30	25 months	25	Pathology cross sectional	NonTumor_pathology	extracted	decreased	Liver pathology	Hepatic sum all other pathologies	Yes	Sham	Male	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	1.7143000	1.6923000	0.5687	0.5687	mean	score	SEM	2.1277858	2.0503857	NA	NA	NA	NA	NA	14	13	unpublished data	Data that was unpublished from Herrara et al. Pathology method included in paper for sham animals	NA	117
Herrara et al 2020	130	31	31	25 months	25	Pathology cross sectional	NonTumor_pathology	extracted	decreased	Liver pathology	Hepatic lipidosis	Yes	Sham	Female	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	1.5714000	1.1250000	0.3738	0.3400	mean	score	SEM	1.3985864	1.3601471	NA	NA	NA	NA	NA	14	16	unpublished data	Data that was unpublished from Herrara et al. Pathology method included in paper for sham animals	NA	118
Herrara et al 2020	131	31	31	25 months	25	Pathology cross sectional	NonTumor_pathology	extracted	decreased	Liver pathology	Hepatic sum all other pathologies	Yes	Sham	Female	Mus musculus	Mice	Het3	no	Laboratory	No	7-8 weeks	7.50	1.0000000	0.8750000	0.3631	0.2562	mean	score	SEM	1.3587324	1.0246951	NA	NA	NA	NA	NA	14	16	unpublished data	Data that was unpublished from Herrara et al. Pathology method included in paper for sham animals	NA	119
Phelan 1995	162	65	65	At death or 27 months if still alive	27	Pathology cross sectional	NonTumor_pathology	extracted	Increased	total non-neoplastic lesions	average number of non-neoplastic lesions	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	7.1000000	7.0000000	1.1	0.7	mean	number of lesions	S.E	4.1158231	2.5238859	NA	NA	NA	NA	NA	14	13	Table 6, chapter 6	lots of info on specific lesions in table 5	67 animals examined. Doesn’t state which specific groups so have assumed t was spread evenly across five groups	120
Wang 2021	122	53	53	14-15months	14.5	sensory function	Sensory function	checked	Increased	Smell preference	odor smell preference test for female urine versus control	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	8months	35.00	7.6700000	9.0800000	1.9	1.1	mean	index	SEM	5.3740115	3.1112698	NA	NA	NA	NA	NA	8	8	fig 5m	NA	NA	121
Wang 2021	127	54	54	18 month	18	sensory function	Sensory function	checked	Increased	Smell preference	odor smell preference test for female urine versus control	Yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	8 months	35.00	6.6192971	5.9996651	1.469891624	1.318452595	mean	min	S.E.M	4.1574813	3.7291471	NA	NA	NA	NA	NA	8	8	Fig s3I	NA	NA	122
Lazenby 1993	73	15	15	24 months	24	sensory function	Sensory function	checked	decreased	vision presence of cataracts	vision cataracts	yes	intact	Female	Ratus ratus	Rat	albino Wistar derived	no	Laboratory	No	5 weeks	5.00	0.1400000	0.0000000	NA	NA	proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	1111	56	text	not sure if the sample size for controls is overestimated because they say how many were studied in total but not how many studied at old age	NA	123
Lazenby 1993	74	16	16	24 months	24	sensory function	Sensory function	checked	decreased	vision presence of cataracts	vision cataracts	yes	intact	Male	Ratus ratus	Rat	albino Wistar derived	no	Laboratory	No	5 weeks	5.00	0.0400000	0.0000000	NA	NA	proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	1111	54	text	not sure if the sample size for controls is overestimated because they say how many were studied in total but not how many studied at old age	NA	124
Phelan 1995	155	65	65	14 months old	14	skeletal Muscle function	Strength/balance	extracted	Increased	balance on a dowel	balance on a dowel	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	82.0603081	67.6986504	11.42083219	10.97037099	mean	seconds	S.E	44.2326929	42.4880641	NA	NA	NA	NA	NA	15	15	fig 4a chapter 6	NA	NA	125
Phelan 1995	156	65	65	20 months old	20	skeletal Muscle function	Strength/balance	extracted	Increased	balance on a dowel	balance on a dowel	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	47.1015357	61.2281183	8.155435963	5.723542117	mean	seconds	S.E	31.5858677	22.1671833	NA	NA	NA	NA	NA	15	15	fig 4a chapter 6	NA	NA	126
Phelan 1995	157	65	65	26 months old	26	skeletal Muscle function	Strength/balance	extracted	Increased	balance on a dowel	balance on a dowel	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	14.1761035	48.2608021	5.19790461	9.237139482	mean	seconds	S.E	20.1313980	35.7752874	NA	NA	NA	NA	NA	15	15	fig 4a chapter 6	NA	NA	127
Garratt 2021	51	33	33	675 to 800 days	24.5	skeletal Muscle function	Strength/balance	checked	Increased	Grip strength	grip strength hanging	yes	intact	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	7 weeks	7.00	29.8100000	32.6900000	2.63	3	mean	sec	SEM	13.9166519	15.8745079	NA	NA	NA	NA	NA	28	28	Fig 2	Also data available for mixed sex environment but as this is the only sample point I have excluded	NA	128
Zakeri et al 2019	150	63	63	23 months	23	skeletal Muscle function	Strength/balance	extracted	Increased	Grip strength	Wire hang test	Yes	Sham	Female	Mus musculus	Mice	NMRI	no	Laboratory	No	10 months	43.00	26.1990000	5.6870000	5.193	1.822	mean	seconds	S.E.M	11.6119010	4.0741159	NA	NA	NA	NA	NA	5	5	Fig 4	NA	NA	129
Zakeri et al 2019	151	64	63	23 months	23	skeletal Muscle function	Strength/balance	extracted	Increased	Grip strength	Wire hang test	Yes	intact	Female	Mus musculus	Mice	NMRI	no	Laboratory	No	10 months	43.00	22.4114736	5.6870000	1.004	1.822	mean	seconds	S.E.M	2.0080000	4.0741159	NA	NA	NA	NA	NA	4	5	Fig 4	NA	NA	130
Phelan 1995	159	65	65	12 months old	12	skeletal Muscle function	Strength/balance	extracted	Increased	Grip strength	Hang time on tread	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	137.8000000	104.2000000	24.4	18.5	mean	hang time, sec	S.E	77.1595749	58.5021367	NA	NA	NA	NA	NA	10	10	Table 3, chapter 6	NA	NA	131
Phelan 1995	160	65	65	18 months old	18	skeletal Muscle function	Strength/balance	extracted	Increased	Grip strength	Hang time on tread	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	15.6000000	20.4000000	5.2	6.2	mean	hang time, sec	S.E	18.0133284	21.4774300	NA	NA	NA	NA	NA	12	12	Table 3, chapter 6	NA	NA	132
Perez 1989	93	27	27	24 months	24	skeletal Muscle function	Strength/balance	checked	Increased	rotarod	rotarod acceleration 4th trial	yes	Sham	Male	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	24h	0.14	6.2400000	24.7500000	1.34	10.49	mean	seconds	SEM	5.3600000	39.2499860	NA	NA	NA	NA	NA	16	14	fig2	NA	NA	133
Garratt 2018	46	30	30	22-24 months	23	skeletal Muscle function	Strength/balance	checked	Increased	rotarod	grip strengthrotarod	yes	Sham	Male	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	48.2100000	71.5900000	7.79	13.88	mean	sec	SEM	29.1475110	53.7570088	NA	NA	NA	NA	NA	14	15	fig5	got exact sample sizes from data files	NA	134
Garratt 2018	49	31	31	22-24 months	23	skeletal Muscle function	Strength/balance	checked	Increased	rotarod	grip strengthrotarod	yes	Sham	Female	Mus musculus	Mice	UM-Het3	no	Laboratory	No	3 months	12.00	63.6500000	57.0000000	12.63	9.37	mean	sec	SEM	47.2571328	37.4800000	NA	NA	NA	NA	NA	14	16	fig5	got exact sample sizes from data files	NA	135
Wang 2021	123	54	54	18 month	18	skeletal Muscle function	Strength/balance	checked	Increased	rotarod	rotarod	Yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	8 months	35.00	21.9600000	25.1500000	1.97	2.45	mean	m	S.E.M	5.5720014	6.9296465	NA	NA	NA	NA	NA	8	8	Fig S3B	NA	NA	136
Wang 2021	120	53	53	14-15months	14.5	skeletal Muscle function	Strength/balance	checked	Increased	Treamill	Treatmill coordination	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	8months	35.00	3.1480000	3.9570000	0.199	0.284	mean	min	SEM	0.5628570	0.8032733	NA	NA	NA	NA	NA	8	8	fig5h	NA	NA	137
Wang 2021	119	53	53	14-15months	14.5	skeletal Muscle function	Strength/balance	checked	Increased	Treamill	Treadmill running	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	8months	35.00	2.1500000	3.0800000	0.2	0.233	mean	joule	SEM	0.5656854	0.6590235	NA	NA	NA	NA	NA	8	8	fig 5G	NA	NA	138
Wang 2021	124	54	54	18 month	18	skeletal Muscle function	Strength/balance	checked	Increased	Treamill	Treadmill running	Yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	8 months	35.00	1.2800000	1.3200000	0.17	0.13	mean	J	S.E.M	0.4808326	0.3676955	NA	NA	NA	NA	NA	8	8	FigS3C	NA	NA	139
Wang 2021	125	54	54	18 month	18	skeletal Muscle function	Strength/balance	checked	Increased	Treamill	Treatmill coordination	Yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	8 months	35.00	2.2700000	2.3300000	0.26	0.32	mean	min	S.E.M	0.7353911	0.9050967	NA	NA	NA	NA	NA	8	8	FigS3D	NA	NA	140
Phelan 1995	158	65	65	28 months old	28	skeletal Muscle function	Strength/balance	checked	Increased	Treamill	Max running speed on treadmill	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	25.7000000	30.3000000	1.8	1.7	mean	m/min	S.E	5.6920998	5.3758720	NA	NA	NA	NA	NA	10	10	p180	NA	NA	141
Mason 2011	81	4	4	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary lesion at death	yes	Sham	Female	Mus musculus	Mice	CBA/J	no	Laboratory	No	21 days	3.00	0.0590000	0.0000000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	34	25	table 1	NA	NA	142
Hotchkiss 1995	61	26	26	at presentation or necropsy	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	intact	Female	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	90 days	13.00	0.4170000	0.0830000	NA	NA	Proportion	proportion	NA	NA	NA	NA	NA	NA	NA	NA	12	12	Fig 3	state that in intact animals the presence of mammory tumors was positively correlated with survival, i.e. These did not cause death	NA	143
durbin 1966	35	28	28	At some point over life	death	Pathology at death	Tumor_mammory	checked. Changed to proportion	decreased	Mammary pathology	mammary tumor incidence	oophorectomy	intact	Female	Ratus ratus	Rat	CD	no	Laboratory	No	77days	11.00	0.6520000	0.0390000	n/a	n/a	Proportion	%	NA	NA	NA	NA	NA	NA	NA	NA	46	45	table 2 &1 for n	NA	NA	144
pilgrim1957	102	53	53	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	intact	Female	Mus musculus	Mice	C3H/HeCRGL	no	Laboratory	No	3 months	12.00	0.9200000	0.6200000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	39	37	table 1	NA	NA	145
pilgrim1957	103	54	53	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	intact	Female	Mus musculus	Mice	C3H/HeCRGL	no	Laboratory	No	4 months	16.00	0.9200000	0.7200000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	39	39	table 1	NA	NA	146
pilgrim1957	104	55	53	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	intact	Female	Mus musculus	Mice	C3H/HeCRGL	no	Laboratory	No	5 months	22.00	0.9200000	0.7300000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	39	37	table 1	NA	NA	147
planas silva 2008	105	56	54	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	Sham	Female	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	1 month	4.00	1.0000000	0.0000000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	12	6	fig4A	NA	NA	148
planas silva 2008	106	57	54	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	intact	Female	Ratus ratus	Rat	sprague-Dawley	no	NA	NA	1 month	4.00	1.0000000	0.0000000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	6	6	fig4A and text	NA	NA	149
planas silva 2008	107	58	55	end of life	death	Pathology at death	Tumor_mammory	checked	decreased	Mammary pathology	mammary tumor incidence	yes	Sham	Female	Ratus ratus	Rat	sprague-Dawley	no	NA	NA	4.5 months	4.50	1.0000000	0.1430000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	7	7	Fig4B and text	NA	NA	150
Holand 1977	192	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Adrenal-Cortical adenoma	Adrenal-Cortical adenoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.0090000	0.0642000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	151
Holand 1977	191	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Harderian adenoma	Harderian adenoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.0090000	0.0490000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	152
Pullinger and Head 1964	183	72	72	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Liver pathology	Percentage hepatoma	yes	intact	Female	Mus musculus	Mice	C3Hf	no	NA	NA	56 to 111 days of age	12.00	0.2450000	0.2500000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	110	32	Table 1	Data is available for breeding females and castrated males, but not relevant control or matched sterilized group	NA	153
Holand 1977	189	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	nonthymic lymphosarcoma	nonthymic lymphosarcoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.1280000	0.0390000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	154
Holand 1977	190	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	nonthymic lymphosarcoma	nonthymic lymphosarcoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.1280000	0.0390000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	155
Hotchkiss 1995	62	26	26	at necrospy	death	Pathology at death	Tumor_nonmammory	checked	decreased	pituitary tumors	pituitary tumors	yes	intact	Female	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	90 days	13.00	0.7500000	0.0000000	NA	NA	Proportion	proportion	NA	NA	NA	NA	NA	NA	NA	NA	8	12	table 1	NA	NA	156
Holand 1977	193	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	pituitary tumors	Pituitary adenoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.0830000	0.0280000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	157
Drori and Folman 1976	168	68	68	End of life	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Proportion with hypophyseal adenoma	Proportion with hypophyseal adenoma	yes	intact	Male	Ratus ratus	Rat	Albino	no	NA	NA	41 days	6.00	0.0850000	0.2170000	NA	NA	proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	46	Table 3	Other pathologies (pneumonia and enteritis) are listed but not included in healthspan search protocol	NA	158
Apelo 2020	7	7	7	At death	death	Pathology at death	Tumor_nonmammory	Yes	decreased	Proportion with tumors	Proportion of animals with cancer at death	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	0.5000000	0.5900000	NA	NA	Proportion	percent with cancer	NA	NA	NA	NA	NA	NA	NA	NA	22	17	fig 5 supplement 1	NA	Mice WT of Rictor flox mice	159
Apelo 2020	8	8	8	At death	death	Pathology at death	Tumor_nonmammory	Yes	decreased	Proportion with tumors	Proportion of animals with cancer at death	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	0.4200000	0.4800000	NA	NA	Proportion	percent with cancer	NA	NA	NA	NA	NA	NA	NA	NA	12	21	fig 5 supplement 1	NA	Mice WT of Rictor flox mice	160
storer 1982	111	17	17	end of life	death	Pathology at death	Tumor_nonmammory	checked	decreased	Proportion with tumors	all sites tumours thymic lymphomas exckluded	yes	intact	Female	Mus musculus	Mice	balb c	no	Laboratory	No	50 days	7.00	0.9040000	0.8530000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	831	326	table 1 and 3	Data is available for lots of individual tumor sites	NA	161
storer 1982	113	18	18	end of life	death	Pathology at death	Tumor_nonmammory	checked	decreased	Proportion with tumors	all sites tumours thymic lymphomas exckluded	yes	intact	Female	Mus musculus	Mice	RFM	no	Laboratory	No	50 days	7.00	0.7620000	0.7710000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	745	337	table4, 6	Data is available for lots of individual tumor sites	NA	162
pullinger 1961	108	59	59	end of life	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Pulmonary adenoma	pulmonary adenoma	yes	intact	Female	Mus musculus	Mice	C3Hf	no	NA	NA	57 to 67 days	9.00	0.1270000	0.0625000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	110	32	End of text for exp 3 results section	some other pathologies are listed but not included in protocol	NA	163
Holand 1977	194	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Pulmonary adenoma	Pulmonary adenoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.2480000	0.3400000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	164
Holand 1977	188	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	Reticulum cell sarcoma	Reticulum cell sarcoma	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.5970000	0.6500000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	165
durbin 1966	36	28	29	At some point over life	death	Pathology at death	Tumor_nonmammory	checked. Changed to proportion	decreased	superficial skin and subcutaneous tumor not mammary	superficial skin and subcutaneous tumor not mammary	oophorectomy	intact	Female	Ratus ratus	Rat	CD	no	Laboratory	No	77days	11.00	0.0547445	0.1363636	NA	NA	Proportion	number	NA	NA	NA	NA	NA	NA	NA	NA	548	44	table 4	excluded data for other skin tumors because control group is not comparable?	NA	166
storer 1982	112	17	17	end of life	death	Pathology at death	Tumor_nonmammory	checked	decreased	thymic lymphomas	thymic lymphomas	yes	intact	Female	Mus musculus	Mice	balb c	no	Laboratory	No	50 days	7.00	0.0140000	0.0210000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	831	326	table 1 and 3	Data is available for lots of individual tumor sites	NA	167
storer 1982	114	18	18	end of life	death	Pathology at death	Tumor_nonmammory	checked	decreased	thymic lymphomas	thymic lymphomas	yes	intact	Female	Mus musculus	Mice	RFM	no	Laboratory	No	50 days	7.00	0.0950000	0.0270000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	745	337	table 2	Data is available for lots of individual tumor sites	NA	168
Holand 1977	187	76	76	At death	death	Pathology at death	Tumor_nonmammory	extracted	decreased	thymic lymphomas	thymic lymphomas	yes	intact	Female	Mus musculus	Mice	RFMf/Wg	no	NA	NA	5-6 weeks	5.50	0.1650000	0.0780000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	109	103	Table 3	Only using data from non-irradiated animals	NA	169
Andervont 1950	184	73	73	15 months	15	Pathology cross sectional	Tumor_nonmammory	extracted	decreased	Liver pathology	Percentage hepatoma	yes	intact	Male	Mus musculus	Mice	C3H	no	NA	NA	30-70 days of age	14.00	0.1200000	0.0000000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	38	36	Table 1	NA	NA	170
Andervont 1950	185	74	74	15 months	15	Pathology cross sectional	Tumor_nonmammory	extracted	decreased	Liver pathology	Percentage hepatoma	yes	intact	Male	Mus musculus	Mice	C3H	no	NA	NA	30-70 days of age, plus 9 up to 160 days old	14.00	0.2700000	0.1800000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	86	60	Table 1	NA	NA	171
Andervont 1950	186	75	75	15 months	15	Pathology cross sectional	Tumor_nonmammory	extracted	decreased	Liver pathology	Percentage hepatoma	yes	intact	Male	Mus musculus	Mice	CBA/J	no	NA	NA	30-70 days of age, plus 4 up to 160 days old	14.00	0.2900000	0.2000000	NA	NA	Proportion	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	32	Table 1	NA	NA	172
Phelan 1995	161	65	65	At death or 27 months if still alive	27	Pathology cross sectional	Tumor_nonmammory	extracted	Increased	Proportion with tumors	average number of neoplastic lesions	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	2.3000000	1.2000000	0.5	0.2	mean	number of lesions	S.E	1.8708287	0.7211103	NA	NA	NA	NA	NA	14	13	Table 6, chapter 6	lots of info on specific lesions in table 5	67 animals examined. Doesn’t state which specific groups so have assumed t was spread evenly across five groups	173
Borbelyova et al 2016	133	77	77	30 months	30	activity, energetics and metabolism	Voluntary activity	extracted	Increased	Open field test	Open field rearing	Yes	unclear	Male	Ratus ratus	Rat	wistar albino	no	Laboratory	No	Day 47	6.50	1.7093772	2.1870866	0.622591267	0.621445956	means	cm	S.E.M	2.4112856	2.3252379	NA	NA	NA	NA	NA	15	14	Fig 1c	NA	NA	174
Borbelyova et al 2016	132	77	77	30 months	30	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	Open field distance moved	Yes	unclear	Male	Ratus ratus	Rat	wistar albino	no	Laboratory	No	Day 47	6.50	977.6000000	639.0000000	159.4	115	means	cm	S.E.M	617.3535454	430.2905995	NA	NA	NA	NA	NA	15	14	Fig 1a	Do we want to include open field test considering it is in an unfamilar environment?	NA	175
Borbelyova et al 2021	136	77	77	30 months	30	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	Open field distance moved	Yes	Sham	Male	Ratus ratus	Rat	wistar albino	no	Laboratory	No	Day 47	6.50	1081.0000000	888.9000000	626.9	456.9	means	cm	SD	626.9000000	456.9000000	NA	NA	NA	NA	NA	7	3	Fig 6	Started with n = 5 for cast but lost 2 during experiment	NA	176
Leffa 2013	77	12	12	18 months	18	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	open field test: number of crossing	yes	Sham	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	55.6000000	47.9300000	5.68	3.05	medium	number	SEM	19.6760972	10.5655099	NA	NA	NA	NA	NA	12	12	fig3	NA	NA	177
Perez 1989	92	27	27	24 months	24	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	open field test rearing	yes	Sham	Male	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	24h	0.14	3.8400000	6.5800000	1.06	1.61	mean	number	SEM	4.2400000	6.0240684	NA	NA	NA	NA	NA	16	14	fig1	Christine had extracted defication boluses but I deletedit as it has nothing to do with activity or ageing	NA	178
Perez 1989	91	27	27	24 months	24	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	open field test segments crossed	yes	Sham	Male	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	24h	0.14	25.2200000	42.2600000	3.06	4.32	mean	number	SEM	12.2400000	16.1639599	NA	NA	NA	NA	NA	16	14	fig1	NA	NA	179
Domonkos 2017	33	35	35	12 months	12	activity, energetics and metabolism	Voluntary activity	checked	Increased	Open field test	open field test (total activity)	yes	Sham	Male	Ratus ratus	Rat	Lewis	no	NA	NA	1 month	4.00	9.5400000	9.7800000	0.95	1.39	mean	m	SEM	4.6540305	6.8095815	NA	NA	NA	NA	NA	24	24	Fig 2	NA	NA	180
de chaves et al 2009	164	67	67	18 months	18	activity, energetics and metabolism	Voluntary activity	extracted	Increased	Open field test	Open field distance number of crossings	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	3 months	12.00	56.3400000	48.2200000	5.33	3.33	mean	crossings	SEM	19.5836705	12.2352013	NA	NA	12 to 15	12 to 15	NA	14	14	Fig 3	NA	NA	181
de chaves et al 2009	165	67	67	18 months	18	activity, energetics and metabolism	Voluntary activity	extracted	Increased	Open field test	Open field distance rearings	yes	Sham	Female	Ratus ratus	Rat	wistar	no	NA	NA	3 months	12.00	16.0800000	17.1000000	2.37	1.73	mean	rearings	SEM	8.7079360	6.3564259	NA	NA	12 to 15	12 to 15	NA	14	14	Fig 3	NA	NA	182
Apelo 2020	1	7	7	12 months	12	activity, energetics and metabolism	Voluntary activity	Yes	Increased	Total activity dark period	activity dark	yes	Sham	Female	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	32.7000000	20.3100000	4.1	2.55	mean	counts (x10 to power 3)	SEM	12.9653384	8.0638080	NA	NA	NA	NA	NA	10	10	fig 4	Just dark period data used as this is when animals are active	Mice WT of Rictor flox mice	183
Apelo 2020	3	8	8	12 months	12	activity, energetics and metabolism	Voluntary activity	Yes	Increased	Total activity dark period	activity dark	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	Laboratory	No	3.5weeks	3.50	19.3600000	20.6300000	2.54	4.13	mean	counts (x10 to power 3)	SEM	8.4242270	13.0602067	NA	NA	NA	NA	NA	11	10	fig 4	Just dark period data used as this is when animals are active	Mice WT of Rictor flox mice	184
Borbelyova et al 2016	135	77	77	30 months	30	activity, energetics and metabolism	Voluntary activity	checked	Increased	voluntary activity assessment	observation cage distance moved	Yes	unclear	Male	Ratus ratus	Rat	wistar albino	no	Laboratory	No	Day 47	6.50	14.2000000	10.2000000	1.6	0.8	means	m	S.E.M	6.1967734	2.9933259	NA	NA	NA	NA	NA	15	14	Fig7A	Do we want to include open field test considering it is in an unfamilar environment?	NA	185
picazo 2016	98	21	21	13 months	13	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary activity assessment	spontaneous activity test: rearing	yes	intact	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	306.3000000	354.5000000	12	22.6	mean	number	SEM	26.8328157	50.5351363	NA	NA	NA	NA	NA	5	5	fig1B	NA	NA	186
picazo 2016	99	21	21	18 months	18	activity, energetics and metabolism	Voluntary activity	checked	Increased	voluntary activity assessment	spontaneous activity test: rearing	yes	intact	Female	Ratus ratus	Rat	wistar	no	Laboratory	No	3 months	12.00	343.5000000	318.2000000	12.6	16.8	mean	number	SEM	28.1744565	37.5659420	NA	NA	NA	NA	NA	5	5	fig1B	NA	NA	187
Domonkos 2017	34	35	35	12 months	12	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary activity assessment	Total distance moved 30 min (phenotyper)	yes	Sham	Male	Ratus ratus	Rat	Lewis	no	NA	NA	1 month	4.00	0.5910000	0.6800000	0.0696	0.0695	mean	m	SEM	0.3409690	0.3404791	NA	NA	NA	NA	NA	24	24	Fig 2	NA	NA	188
Wang 2021	118	53	53	14-15months	14.5	activity, energetics and metabolism	Voluntary activity	checked	Increased	voluntary activity assessment	locomotion (Assume voluntary?)	yes	Sham	Male	Mus musculus	Mice	C57Bl6/J	no	NA	NA	8months	35.00	26.1500000	39.2200000	2.18	4.79	mean	m	SEM	6.1659711	13.5481659	NA	NA	NA	NA	NA	8	8	fig 5 F	NA	NA	189
Perez 1989	94	27	27	24 months	24	activity, energetics and metabolism	Voluntary activity	checked	Increased	voluntary wheel running	runnning wheel percent dark period	yes	Sham	Male	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	24h	0.14	77.8000000	83.5500000	2.93	2.15	mean	%activity	SEM	11.7200000	8.0445634	NA	NA	NA	NA	NA	16	14	fig3	NA	NA	190
Perez 1989	95	27	27	24 months	24	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary wheel running	runnning wheel total	yes	Sham	Male	Ratus ratus	Rat	sprague-Dawley	no	Laboratory	No	24h	0.14	3819.1531460	4563.9592400	465.5223585	501.9538979	mean	Total counts	SEM	1862.0894340	1878.1395100	NA	NA	NA	NA	NA	16	14	fig3	NA	NA	191
Phelan 1995	152	65	65	14 months old	14	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary wheel running	voluntary wheel running	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	4.8245627	2.4727543	0.533037655	0.349474053	mean	km/day	S.E	2.9195675	1.9141482	NA	NA	NA	NA	NA	30	30	Fig 3 chapter 6	stated this was measured on all animals. Looked at how many died up to each age point to estimate sample size	NA	192
Phelan 1995	153	65	65	20 months old	20	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary wheel running	voluntary wheel running	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	3.1996265	2.1559313	0.427177101	0.294472083	mean	km/day	S.E	1.8123589	1.5301218	NA	NA	NA	NA	NA	18	27	Fig 3 chapter 6	stated this was measured on all animals. Looked at how many died up to each age point to estimate sample size	NA	193
Phelan 1995	154	65	65	26 months old	26	activity, energetics and metabolism	Voluntary activity	extracted	Increased	voluntary wheel running	voluntary wheel running	Yes	Sham	Female	Mus musculus	Mice	Swiss Webster	no	Laboratory	No	weaning	3.00	2.4942007	1.8164948	0.428577681	0.33514728	mean	km/day	S.E	1.6035908	1.4608731	NA	NA	NA	NA	NA	14	19	Fig 3 chapter 6	stated this was measured on all animals. Looked at how many died up to each age point to estimate sample size	NA	194

Calculating effect size for healthspan data

# getting CV2 to get lnRR

dat %>% group_by(Study) %>% 
  summarise(cv2_cont = mean((Error_control_SD/Control_value)^2, na.rm = T), 
            cv2_trt = mean((Error_experimental_SD/Experimental_value)^2, na.rm = T), 
            n_cont = mean(Sample_size_control, na.rm = T), 
            n_trt =  mean(Sample_size_experimental, na.rm = T)) %>% 
  ungroup() %>% 
  summarise(cv2_cont = weighted.mean(cv2_cont, n_cont, na.rm = T), 
            cv2_trt = weighted.mean(cv2_trt, n_trt, na.rm = T)) -> cvs

# lnRR

# survival proportion using arcsin transformation = lnrrp
# using CV
dat$yi <- ifelse(effect_type == "other", lnrrm(dat$Experimental_value,
                                               dat$Control_value, 
                                               dat$Sample_size_experimental, 
                                               dat$Sample_size_control,
                                               cvs[["cv2_trt"]],
                                               cvs[["cv2_cont"]])[[1]],
                                         lnrrp(dat$Experimental_value, 
                                               dat$Control_value, 
                                               dat$Sample_size_experimental, 
                                               dat$Sample_size_control)[[1]])

dat$vi <- ifelse(effect_type == "other", lnrrm(dat$Experimental_value,
                                               dat$Control_value, 
                                               dat$Sample_size_experimental, 
                                               dat$Sample_size_control, 
                                               cvs[["cv2_trt"]],
                                               cvs[["cv2_cont"]])[[2]],
                                         lnrrp(dat$Experimental_value, 
                                               dat$Control_value, 
                                               dat$Sample_size_experimental, 
                                               dat$Sample_size_control)[[2]])

# for plotting without using CV

dat$vi2 <- ifelse(effect_type == "other", lnrrm2(dat$Experimental_value,
                                               dat$Control_value, 
                                               dat$Sample_size_experimental, 
                                               dat$Sample_size_control, 
                                               dat$Error_experimental_SD,
                                               dat$Error_control_SD)[[2]],
                                        lnrrp(dat$Experimental_value, 
                                              dat$Control_value, 
                                              dat$Sample_size_experimental, 
                                              dat$Sample_size_control)[[2]])



# flipping directions

dat$direction <- ifelse(dat$direction.of.improved.health == "Increased", 1, -1)

dat$yi <- dat$yi * dat$direction

15 Meta-analysis

15.1 Main model

# meta-analysis

VCV <- vcalc(vi = vi, 
             cluster = Study, 
             obs = Effect_ID, #subgroup = Measure,
             data = dat, 
             rho = 0.5)

mod <-  rma.mv(yi = yi, 
               V = VCV, 
               random = list(~1|Strain, 
                             ~ 1|Study, 
                             ~ 1|Effect_ID
                             ), 
               data = dat, 
               test = "t",
               sparse = TRUE,
               control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod) 

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-127.4764   254.9528   262.9528   276.0036   263.1656   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0011  0.0336     23     no     Strain 
sigma^2.2  0.0000  0.0001     48     no      Study 
sigma^2.3  0.0774  0.2783    194     no  Effect_ID 

Test for Heterogeneity:
Q(df = 193) = 512.3283, p-val < .0001

Model Results:

estimate      se    tval   df    pval   ci.lb   ci.ub    
  0.1095  0.0498  2.2012  193  0.0289  0.0114  0.2077  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod),5) # almost no phylogenetic effect

    I2_Total    I2_Strain     I2_Study I2_Effect_ID 
    71.19775      1.02545      0.00001     70.17229 

# visualizing the result
orchard_plot(mod, xlab = "log response ratio (lnRR)", group = "Study")

16 Meta-regression

Sex difference (Sex)

mod1 <-  rma.mv(yi = yi, 
                V = VCV, 
                mod = ~ Sex - 1, 
                random = list(~ 1|Strain, 
                              ~ 1|Study, 
                              ~ 1|Effect_ID), 
                #struct = "DIAG",
                data = dat, 
                test = "t",
                sparse = TRUE,
                control=list(optimizer="optim", optmethod="BFGS")
)
summary(mod1) 

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-126.9820   253.9641   263.9641   280.2516   264.2867   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0041  0.0642     23     no     Strain 
sigma^2.2  0.0000  0.0016     48     no      Study 
sigma^2.3  0.0776  0.2785    194     no  Effect_ID 

Test for Residual Heterogeneity:
QE(df = 192) = 510.4776, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 192) = 2.3418, p-val = 0.0989

Model Results:

           estimate      se    tval   df    pval    ci.lb   ci.ub    
SexFemale    0.0991  0.0595  1.6668  192  0.0972  -0.0182  0.2165  . 
SexMale      0.1349  0.0806  1.6736  192  0.0958  -0.0241  0.2939  . 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# visualizing the result

mod_healthspan_sex <- mod_results(mod1, group = "Study", mod = "Sex", data = dat)

#saveRDS(mod_healthspan_sex, here("Rdata", "fig", "mod_healthspan_sex.rds"))

#mod_healthspan_sex <- readRDS(here("Rdata", "fig", "mod_healthspan_sex.rds"))        
        
orchard_plot(mod_healthspan_sex, mod = "Sex", 
             xlab = "log response ratio (lnRR)", group = "Study", cb = F) # colour = T)

# attr(lm_result, "class") <- NULL

# orchard plot

main <- mod_results(mod, group = "Study")
attr(main, "class") <- NULL
main$mod_table$name <- gsub("Intrcpt", "Overall", main$mod_table$name)
main$mod_table$name <- factor(main$mod_table$name)
main$data$moderator <- gsub("Intrcpt", "Overall", main$data$moderator)
main$data$moderator <- factor(main$data$moderator)

class(main) <- c("orchard", "data.frame")

p_overall <- orchard_plot(main, xlab = "log response ratio (lnRR)", angle = 0, group = "Study") + ylim(-2.2,2)


sex_diff <- mod_results(mod1, mod = "Sex", group = "Study")


p_sex_diff <-orchard_plot(sex_diff, mod = "Sex", 
                          xlab = "log response ratio (lnRR)", group = "Study", angle = 0, cb = F) + ylim(-2.2,2)

combined <- submerge(sex_diff, main)

# changing the name (intercept) to:
# TODO - this should be done in the orchard_plot function
combined$mod_table$name <- gsub("Intrcpt", "Overall", combined$mod_table$name)
combined$mod_table$name <- factor(combined$mod_table$name)

combined$data$moderator <- gsub("Intrcpt", "Overall", combined$data$moderator)


orchard_plot(combined,
             xlab = "log response ratio (lnRR)", group = "Study", angle = 0, cb = F) + 
  scale_colour_manual(values = rev(c("#999999", "#88CCEE", "#CC6677"))) +
  scale_fill_manual(values = rev(c("#999999", "#88CCEE", "#CC6677")))

Measuremeta types (Sub.measure)

mod2 <-  rma.mv(yi = yi, 
                V = VCV, 
                mod = ~ Sub.measure - 1, 
                random = list(~ 1|Strain, 
                              ~ 1|Study, 
                              ~ 1|Effect_ID), 
                data = dat, 
                test = "t",
                sparse = TRUE,
                control=list(optimizer="optim", optmethod="BFGS")
)
summary(mod2) 

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-108.0054   216.0108   248.0108   299.1868   251.3279   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0113  0.1061     23     no     Strain 
sigma^2.2  0.0000  0.0016     48     no      Study 
sigma^2.3  0.0616  0.2483    194     no  Effect_ID 

Test for Residual Heterogeneity:
QE(df = 181) = 419.9578, p-val < .0001

Test of Moderators (coefficients 1:13):
F(df1 = 13, df2 = 181) = 2.5436, p-val = 0.0031

Model Results:

                                       estimate      se     tval   df    pval 
Sub.measureCardiac function/pathology   -0.0291  0.1873  -0.1552  181  0.8769 
Sub.measureCardiac size                  0.1325  0.1712   0.7738  181  0.4401 
Sub.measureCognition                     0.2027  0.1581   1.2816  181  0.2016 
Sub.measureFrailty                       0.3243  0.6384   0.5080  181  0.6121 
Sub.measureImmune Function              -1.0856  0.6389  -1.6992  181  0.0910 
Sub.measureMetabolism                   -0.0135  0.2586  -0.0521  181  0.9585 
Sub.measureMuscle size                   0.1732  0.3513   0.4931  181  0.6225 
Sub.measureNonTumor_pathology            0.0066  0.1039   0.0640  181  0.9491 
Sub.measureSensory function              0.3828  0.2681   1.4276  181  0.1551 
Sub.measureStrength/balance              0.3398  0.1634   2.0788  181  0.0390 
Sub.measureTumor_mammory                 0.5965  0.1439   4.1464  181  <.0001 
Sub.measureTumor_nonmammory              0.0381  0.0781   0.4873  181  0.6266 
Sub.measureVoluntary activity           -0.1880  0.1383  -1.3595  181  0.1757 
                                         ci.lb   ci.ub      
Sub.measureCardiac function/pathology  -0.3987  0.3406      
Sub.measureCardiac size                -0.2054  0.4703      
Sub.measureCognition                   -0.1094  0.5147      
Sub.measureFrailty                     -0.9354  1.5840      
Sub.measureImmune Function             -2.3462  0.1750    . 
Sub.measureMetabolism                  -0.5238  0.4969      
Sub.measureMuscle size                 -0.5199  0.8664      
Sub.measureNonTumor_pathology          -0.1984  0.2117      
Sub.measureSensory function            -0.1462  0.9118      
Sub.measureStrength/balance             0.0173  0.6622    * 
Sub.measureTumor_mammory                0.3126  0.8804  *** 
Sub.measureTumor_nonmammory            -0.1161  0.1922      
Sub.measureVoluntary activity          -0.4608  0.0849      

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# visualizing the result
orchard_plot(mod2, mod = "Sub.measure", 
             xlab = "log response ratio (lnRR)", group = "Study", angle = 45)

Sex diff. x meausremetn types (MesSex)

# Measure x Sex
# need to name up Sub.measure
dat$Sub.measure <- factor(dat$Sub.measure, 
                          levels = levels(dat$Sub.measure),
                          labels = c("Cardiac\nfunction/\npathology", "Cardiac size", "Cognition", "Frailty",
                                     "Immune\nfunction", "Metabolism", "Muscle size", "Non-tumor\npathology",
                                     "Sensory\nfunction", "Strength/\nbalance", "Tumor\nmammory", "Tumor\nnonmammory",
                                     "Voluntary\nactivity"))


dat$MesSex <- paste0(dat$Sub.measure, "_", dat$Sex)

dat$Measurement.type <- factor(dat$Measurement.type,
                           levels = levels(as.factor(dat$Measurement.type)),
                           labels = levels(as.factor(dat$Measurement.type))
                           )


mod3 <-  rma.mv(yi = yi, 
                 V = vi, 
                 mod = ~ MesSex - 1, 
                 random = list(~ 1|Strain, 
                               ~ 1|Study, 
                               ~ 1|Effect_ID), 
                 data = dat, 
                 test = "t",
                 sparse = TRUE,
                 control=list(optimizer="optim", optmethod="BFGS")
)

summary(mod3)

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-113.8147   227.6294   279.6294   361.3126   289.3794   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0323  0.1797     23     no     Strain 
sigma^2.2  0.0000  0.0015     48     no      Study 
sigma^2.3  0.0474  0.2177    194     no  Effect_ID 

Test for Residual Heterogeneity:
QE(df = 171) = 250.4550, p-val < .0001

Test of Moderators (coefficients 1:23):
F(df1 = 23, df2 = 171) = 1.8556, p-val = 0.0140

Model Results:

                                            estimate      se     tval   df 
MesSexCardiac\nfunction/\npathology_Female    0.0070  0.2308   0.0304  171 
MesSexCardiac\nfunction/\npathology_Male     -0.0104  0.1820  -0.0574  171 
MesSexCardiac size_Female                     0.1699  0.2052   0.8278  171 
MesSexCardiac size_Male                       0.1279  0.1647   0.7769  171 
MesSexCognition_Female                       -0.0766  0.1549  -0.4947  171 
MesSexCognition_Male                          0.4879  0.2396   2.0357  171 
MesSexFrailty_Male                            0.3398  0.6288   0.5404  171 
MesSexImmune\nfunction_Female                -1.0961  0.3740  -2.9309  171 
MesSexMetabolism_Female                      -0.0847  0.3806  -0.2226  171 
MesSexMetabolism_Male                        -0.0016  0.2996  -0.0052  171 
MesSexMuscle size_Female                     -0.0847  0.4576  -0.1851  171 
MesSexMuscle size_Male                        0.1371  0.4204   0.3262  171 
MesSexNon-tumor\npathology_Female            -0.0568  0.1184  -0.4795  171 
MesSexNon-tumor\npathology_Male               0.1456  0.2273   0.6408  171 
MesSexSensory\nfunction_Female                0.8268  0.4130   2.0021  171 
MesSexSensory\nfunction_Male                  0.1313  0.3246   0.4045  171 
MesSexStrength/\nbalance_Female               0.0486  0.2320   0.2093  171 
MesSexStrength/\nbalance_Male                 0.3393  0.2214   1.5322  171 
MesSexTumor\nmammory_Female                   0.6855  0.1592   4.3046  171 
MesSexTumor\nnonmammory_Female                0.0097  0.0965   0.1001  171 
MesSexTumor\nnonmammory_Male                  0.0866  0.1453   0.5956  171 
MesSexVoluntary\nactivity_Female             -0.3630  0.2093  -1.7342  171 
MesSexVoluntary\nactivity_Male                0.0080  0.1755   0.0454  171 
                                              pval    ci.lb    ci.ub      
MesSexCardiac\nfunction/\npathology_Female  0.9758  -0.4487   0.4627      
MesSexCardiac\nfunction/\npathology_Male    0.9543  -0.3696   0.3488      
MesSexCardiac size_Female                   0.4089  -0.2352   0.5750      
MesSexCardiac size_Male                     0.4383  -0.1972   0.4531      
MesSexCognition_Female                      0.6214  -0.3823   0.2291      
MesSexCognition_Male                        0.0433   0.0148   0.9609    * 
MesSexFrailty_Male                          0.5896  -0.9015   1.5811      
MesSexImmune\nfunction_Female               0.0038  -1.8342  -0.3579   ** 
MesSexMetabolism_Female                     0.8241  -0.8360   0.6666      
MesSexMetabolism_Male                       0.9958  -0.5929   0.5898      
MesSexMuscle size_Female                    0.8534  -0.9879   0.8185      
MesSexMuscle size_Male                      0.7447  -0.6927   0.9670      
MesSexNon-tumor\npathology_Female           0.6322  -0.2905   0.1770      
MesSexNon-tumor\npathology_Male             0.5225  -0.3030   0.5943      
MesSexSensory\nfunction_Female              0.0469   0.0116   1.6419    * 
MesSexSensory\nfunction_Male                0.6863  -0.5094   0.7720      
MesSexStrength/\nbalance_Female             0.8344  -0.4094   0.5065      
MesSexStrength/\nbalance_Male               0.1273  -0.0978   0.7764      
MesSexTumor\nmammory_Female                 <.0001   0.3711   0.9998  *** 
MesSexTumor\nnonmammory_Female              0.9204  -0.1808   0.2001      
MesSexTumor\nnonmammory_Male                0.5522  -0.2003   0.3734      
MesSexVoluntary\nactivity_Female            0.0847  -0.7761   0.0502    . 
MesSexVoluntary\nactivity_Male              0.9638  -0.3385   0.3544      

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# visualizing the result

orchard_plot(mod3, mod = "MesSex", 
             xlab = "log response ratio (lnRR)", group = "Study", angle = 0, cb = F)

Figure code

# figures


# some experiments 

res3 <- mod_results(mod3, mod = "MesSex", group = "Study")

mod_table_m <- res3$mod_table[c(2,4,6, 7, 10, 12, 14, 16, 18, 21, 23), ]
mod_table_f <- res3$mod_table[c(1,3,5, 8, 9, 11, 13, 15, 17, 19, 20, 22), ]

data_m <- res3$data[ !is.na(match(res3$data$moderator, mod_table_m$name)), ]
data_f <- res3$data[ !is.na(match(res3$data$moderator, mod_table_f$name)), ]
  
res3_male <- list(mod_table = mod_table_m, data = data_m)
res3_female <- list(mod_table = mod_table_f, data = data_f)

# changing names
# male
res3_male$mod_table$name <- gsub("_Male", "", res3_male$mod_table$name)
res3_male$mod_table$name <- factor(res3_male$mod_table$name)

res3_male$data$moderator <- gsub("_Male", "", res3_male$data$moderator)

# female
res3_female$mod_table$name <- gsub("_Female", "", res3_female$mod_table$name)
res3_female$mod_table$name <- factor(res3_female$mod_table$name)

res3_female$data$moderator <- gsub("_Female", "", res3_female$data$moderator)

class(res3_male) <- c("orchard", "data.frame")
class(res3_female) <- c("orchard", "data.frame")





p_male <- orchard_plot(res3_male, mod = "MesSex", 
             xlab = "log response ratio (lnRR)", group = "Study", angle = 0, cb = F) + labs(title = "Male") + ylim(-2.2,2)


# p_male <- p_male + plot_annotation(title = "Male",
#                          theme = theme(plot.title = element_text(size = 20)))

p_female <- orchard_plot(res3_female, mod = "MesSex", 
             xlab = "log response ratio (lnRR)", group = "Study", angle = 0, cb = F) + labs(title = "Female") + ylim(-2.2,2)

# p_female <- p_female + plot_annotation(title = "Female",
#                          theme = theme(plot.title = element_text(size = 20)))

p_overall + p_sex_diff + p_female + p_male +  plot_layout(widths = c(2, 2), heights = c(1,2.5)) 


# creating a forest plot using dat

str(dat)

# selecting

fdat <- dat %>% select(Study, Measure, Effect_ID, Measurement.type, Sub.measure, yi, vi)

# add lower.ci and upper.ci

fdat <- dat %>% mutate(lower.ci = yi - 1.96*sqrt(vi), upper.ci = yi + 1.96*sqrt(vi))

# p_forest <- ggplot(data = fdat, aes(x = yi, y = Measurement.type)) +
#   geom_errorbarh(aes(xmin = lower.ci, xmax = upper.ci, colour = Sub.measure), 
#                  height = 1, show.legend = TRUE, size = 1, alpha = 0.8, position = position_dodge2(width = 1)) +
#   geom_point(aes(col = Sub.measure), fill = "white", size = 2, shape = 21, position =position_dodge2(width = 1)) +
#   geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
#   geom_vline(xintercept = mod$b, linetype = 2, colour = "red", alpha = 0.3) +
#   labs(x = "lnRR (effect size)", y = "")

fdat$Measurement.type <- factor(
  fdat$Measurement.type,
  levels = unique(fdat$Measurement.type)
)

dodge <- position_dodge2(width = 0.6, preserve = "single")

p_forest <- ggplot(data = fdat, aes(x = yi, y = Measurement.type)) +
  geom_errorbarh(
    aes(xmin = lower.ci, xmax = upper.ci, colour = Sub.measure), 
    height = 0.3,
    position = dodge,
    size = 1, alpha = 0.8
  ) +
  geom_point(
    aes(colour = Sub.measure),
    fill = "white",
    shape = 21,
    size = 2,
    position = dodge
  ) +
  geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
  geom_vline(xintercept = mod$b, linetype = 1, linewidth = 2, colour = "red", alpha = 0.3) +
  scale_y_discrete(expand = c(0,0)) +
  # Rename legend title
  scale_colour_discrete(name = "Sub-measure") +
  labs(x = "lnRR (effect size)", y = "") +
  theme_minimal() +
  # Place legend below plot
  theme(
    legend.position = "bottom",
    axis.text.y = element_text(size = 9)
  )

p_forest

########################
# different drawing .... 
########################

# meta-analytic mean

res <- mod_results(mod, group = "Study", data = dat)[[1]]

saveRDS(res, here("Rdata", "fig", "res.rds"))

# example....
dat$MestypeSex <- paste0(dat$Measurement.type, "_", dat$Sex)

hdat <- escalc(yi = yi, vi = vi2, data = dat)

hdat$es_ID <- factor(1:nrow(hdat))

# MesSex
idat <- aggregate(x = hdat, cluster = MesSex, obs = es_ID, rho = 0)

# MestypeSex

jdat <- aggregate(x = hdat, cluster = MestypeSex, obs = es_ID, rho = 0)

jdat$MestypeSex


# # adding non-aggredated ones
# cdat <- rbind(cdat, bdat[bdat$MesSex == "Frailty_Male", ], bdat[bdat$MesSex == "Sensory\nfunction_Female", ])
# 
# # replacing vi with vi2
# cdat$vi[cdat$MesSex == "Frailty_Male"] <- cdat$vi2[cdat$MesSex == "Frailty_Male"]
# cdat$vi[cdat$MesSex == "Sensory\nfunction_Female"] <- cdat$vi2[cdat$MesSex == "Sensory\nfunction_Female"]
# 
dim(dat)
dim(idat)
dim(jdat)

# CI1
idat$lower.ci <- idat$yi - sqrt(idat$vi) * qnorm(0.975) 
idat$upper.ci <- idat$yi + sqrt(idat$vi) *  qnorm(0.975)

jdat$lower.ci <- jdat$yi - sqrt(jdat$vi) * qnorm(0.975) 
jdat$upper.ci <- jdat$yi + sqrt(jdat$vi) *  qnorm(0.975)




# adding more informaition
idat %>% select(Sub.measure, yi, lower.ci, upper.ci, Sex) -> kdat
# adding more informaition
jdat %>% select(Measurement.type, yi, lower.ci, upper.ci, Sex) -> ldat

addition <- data.frame(Sub.measure = "Overall", yi =  NA, lower.ci = NA, upper.ci = NA, Sex = "Female")
addition1 <- data.frame(Measurement.type = "Overall", yi =  NA, lower.ci = NA, upper.ci = NA, Sex = "Female")


kdat <- rbind(kdat, addition)
ldat <- rbind(ldat, addition1)

# removing sensory function and immune function from kdat

# saving these data
saveRDS(kdat, here("Rdata", "fig", "kdat.rds"))
saveRDS(ldat, here("Rdata", "fig", "ldat.rds"))

####### Start here

res <- readRDS(here("Rdata", "fig", "res.rds"))
kdat <- readRDS(here("Rdata", "fig", "kdat.rds"))
ldat <- readRDS(here("Rdata", "fig", "ldat.rds"))


sum_data <- data.frame("x.diamond" = c(res$lowerCL,
                                       res$estimate ,
                                       res$upperCL,
                                       res$estimate ),
                       "y.diamond" = c(1,
                                       1 + 0.25,
                                       1,
                                       1 - 0.25)
)

# plotting Sub.measure
# looking at 
#dat$Sub.measure 
kdat$Sub.measure <- factor(kdat$Sub.measure,
                          levels = c( "Overall", 
                                      "Cardiac\nfunction/\npathology", 
                                      "Cardiac size", 
                                      "Cognition", 
                                      "Frailty",
                                      "Immune\nfunction", 
                                      "Metabolism", 
                                      "Muscle size", 
                                      "Non-tumor\npathology",
                                      "Sensory\nfunction", 
                                      "Strength/\nbalance",
                                      "Tumor\nmammory", 
                                      "Tumor\nnonmammory",
                                      "Voluntary\nactivity"),
                          labels = c("Overall", 
                                     "Cardiac\nfunction/\npathology", 
                                     "Cardiac size", 
                                     "Cognition", 
                                     "Frailty",
                                     "Immune\nfunction", 
                                     "Metabolism", 
                                     "Muscle size", 
                                     "Non-tumor\npathology",
                                     "Sensory\nfunction", 
                                     "Strength/\nbalance",
                                     "Tumor\nmammory", 
                                     "Tumor\nnonmammory",
                                     "Voluntary\nactivity"))

mes_sex <- ggplot(data = kdat, aes(x = yi, y = Sub.measure)) +
  geom_errorbarh(aes(xmin = lower.ci, xmax = upper.ci, colour = Sex), 
                 height = 0, show.legend = TRUE, size = 4.5, 
                 alpha = 0.8, position =position_dodge(width = 0.75)) +
  geom_point(aes(col = Sex), fill = "white", size = 2, shape = 21, position =position_dodge2(width = 0.75)) +
  geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
  geom_vline(xintercept = res$estimate, linetype = 1, colour = "red", alpha = 0.3) +
  xlim(-1.6, 1.6) +
  #creating 95% prediction intervals
  geom_segment(data = res, ggplot2::aes(x = lowerPR, y = 1, xend = upperPR, yend = 1, group = name)) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data, ggplot2::aes(x = x.diamond, y = y.diamond), fill = "red") +
  
  theme_bw() +
  scale_color_manual(values = c("#CC6677", "#88CCEE")) +
  labs(x = "lnRR (effect size)", y = "", colour = "Sex") +
  theme(legend.position = c(0.95, 0.85),
        legend.justification = c(1, 0)) +
  theme(legend.title = element_text(size = 9)) +
  #theme(legend.direction="horizontal") +
  theme(axis.text.y = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour ="black",
                                   hjust = 0.5)) 


# icons 

filenames <- list.files(here("icons", "literature"), pattern=".png", full.names=TRUE)
ldf <- lapply(filenames, readPNG)
names(ldf) <- substr(filenames, 99, 99+60)

mes_sex1 <- mes_sex +
  annotation_custom(rasterGrob(ldf$Mus_musculus.png), xmin = -2, xmax = -1, ymin = 12, ymax = 13.5) +
  annotation_custom(rasterGrob(ldf$Rattus_argentiventer.png), xmin = -1.5, xmax = -0.5, ymin = 11.5, ymax = 13)

mes_sex1

###

mes_sex2 <- ggplot(data = kdat, aes(x = yi, y = Sub.measure)) +
  geom_errorbarh(aes(xmin = lower.ci, xmax = upper.ci, colour = Sex), 
                 height = 0, show.legend = TRUE, size = 4.5, 
                 alpha = 0.8, position =position_dodge(width = 0.75)) +
  geom_point(aes(col = Sex), fill = "white", size = 2, shape = 21, position =position_dodge2(width = 0.75)) +
  geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
  geom_vline(xintercept = res$estimate, linetype = 1, colour = "red", alpha = 0.3) +
   coord_cartesian(xlim = c(-0.5, 0.7)) +
  #creating 95% prediction intervals
  geom_segment(data = res, ggplot2::aes(x = lowerPR, y = 1, xend = upperPR, yend = 1, group = name)) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data, ggplot2::aes(x = x.diamond, y = y.diamond), fill = "red") +
  
  theme_bw() +
  scale_color_manual(values = c("#CC6677", "#88CCEE")) +
  labs(x = "lnRR (effect size)", y = "", colour = "Sex") +
  theme(legend.position = c(0.95, 0.85),
        legend.justification = c(1, 0)) +
  theme(legend.title = element_text(size = 9)) +
  #theme(legend.direction="horizontal") +
  theme(axis.text.y = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour ="black",
                                   hjust = 0.5)) 
# icons
mes_sex21 <- mes_sex2 +
  annotation_custom(rasterGrob(ldf$Mus_musculus.png), xmin = -0.5, xmax = -0.25, ymin = 12, ymax = 13.5) +
  annotation_custom(rasterGrob(ldf$Rattus_argentiventer.png), xmin = -0.3, xmax = -0.05, ymin = 11.5, ymax = 13)

mes_sex21

# the version with arrows

library(dplyr)

kdat <- kdat %>%
  mutate(
    lower.truncated = lower.ci < -0.5,
    upper.truncated = upper.ci > 0.7
  )

ggplot(data = kdat, aes(x = yi, y = Sub.measure)) +
  geom_errorbarh(aes(xmin = pmax(lower.ci, -0.5), 
                     xmax = pmin(upper.ci, 0.7), 
                     colour = Sex), 
                 height = 0, show.legend = TRUE, size = 4.5, 
                 alpha = 0.8, position = position_dodge(width = 0.75)) +
  
  geom_point(aes(col = Sex), fill = "white", size = 2, shape = 21, position = position_dodge2(width = 0.75)) +
  
  geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
  geom_vline(xintercept = res$estimate, linetype = 1, colour = "red", alpha = 0.3) +
  
  # Add left arrows
  geom_text(data = subset(kdat, lower.truncated),
            aes(x = -0.5, label = "<", y = Sub.measure),
            position = position_dodge(width = 0.75),
            vjust = -0.5, size = 6, colour = "black") +
  
  # Add right arrows
  geom_text(data = subset(kdat, upper.truncated),
            aes(x = 0.7, label = ">", y = Sub.measure),
            position = position_dodge(width = 0.75),
            vjust = -0.5, size = 6, colour = "black") +
  
  coord_cartesian(xlim = c(-0.5, 0.7)) +
  
  geom_segment(data = res, aes(x = lowerPR, y = 1, xend = upperPR, yend = 1, group = name)) +
  geom_polygon(data = sum_data, aes(x = x.diamond, y = y.diamond), fill = "red") +
  
  theme_bw() +
  scale_color_manual(values = c("#CC6677", "#88CCEE")) +
  labs(x = "lnRR (effect size)", y = "", colour = "Sex") +
  
  theme(
    legend.position = c(0.95, 0.85),
    legend.justification = c(1, 0),
    legend.title = element_text(size = 9),
    axis.text.y = element_text(size = 10, colour = "black", hjust = 0.5)
  )


## need to do a version for Measurement.type

#gdat$S
ldat$Measurement.type <- factor(ldat$Measurement.type,
                                
                           levels = c("Overall",
  # Survival & Frailty
  "Frailty score",
  "Energy expenditure",
  "voluntary activity assessment",
  "voluntary wheel running",
  "Total activity dark period",
  
  # Cardiac
  "Ejection fraction",
  "fractional shortening",
  "cardiac fibrosis",
  "Cardiac pathology",
  "Cardiomyocyte size",
  "E/A ratio",
  "Heart size",
  "Left Ventricular Isovolumic Relaxation Time",
  "left ventricle size",
  
  # Cognitive & Behavioral
  "Morris water maze",
  "Barnes maze test",
  "water radial arm maze test",
  "Y maze testing",
  "RAM memory test",
  "novel object recognition",
  "Autoshaping learning test",
  "inhibitory avoidance test",
  "T maze test",
  "Open field test",
  "balance on a dowel",
  "rotarod",
  "Treamill",
  
  # Muscle
  "Grip strength",
  "Skeletal muscle fiber size",
  "Quadriceps size",
  
  # Metabolic
  "Glucose tolerance",
  "Insulin sensitivity",
  
  # Immune & Tumors
  "T cell function test",
  "nonthymic lymphosarcoma",
  "Mammary pathology",
  "pituitary tumors",
  "Pulmonary adenoma",
  "Reticulum cell sarcoma",
  "Polyarteritis",
  "Proportion with tumors ",
  "Proportion with hypophyseal adenoma",
  "Harderian adenoma",
  "Adrenal-Cortical adenoma",
  "thymic lymphomas",
  "superficial skin and subcutaneous tumor not mammary",
  "total non-neoplastic lesions",
  
  # Organ-specific
  "Liver pathology",
  "Kidney pathology",
  
  # Sensory
  "vision presence of cataracts",
  "Smell preference"
),

labels =c("Overall",
  # Survival & Frailty
  "Frailty score",
  "Energy expenditure",
  "Voluntary activity assessment",
  "Voluntary wheel running",
  "Total activity dark period",
  
  # Cardiac
  "Ejection fraction",
  "Fractional shortening",
  "Cardiac fibrosis",
  "Cardiac pathology",
  "Cardiomyocyte size",
  "E/A ratio",
  "Heart size",
  "Left ventricular isovolumic relaxation time",
  "Left ventricle size",
  
  # Cognitive & Behavioral
  "Morris water maze",
  "Barnes maze test",
  "Water radial arm maze test",
  "Y maze testing",
  "RAM memory test",
  "Novel object recognition",
  "Autoshaping learning test",
  "Inhibitory avoidance test",
  "T maze test",
  "Open field test",
  "Balance on a dowel",
  "Totarod",
  "Treadmill",
  
  # Muscle
  "Grip strength",
  "Skeletal muscle fiber size",
  "Quadriceps size",
  
  # Metabolic
  "Glucose tolerance",
  "Insulin sensitivity",
  
  # Immune & Tumors
  "T cell function test",
  "Nonthymic lymphosarcoma",
  "Mammary pathology",
  "Pituitary tumors",
  "Pulmonary adenoma",
  "Reticulum cell sarcoma",
  "Polyarteritis",
  "Proportion with tumors ",
  "Proportion with hypophyseal adenoma",
  "Harderian adenoma",
  "Adrenal-Cortical adenoma",
  "thymic lymphomas",
  "Superficial skin and subcutaneous tumor not mammary",
  "Total non-neoplastic lesions",
  
  # Organ-specific
  "Liver pathology",
  "Kidney pathology",
  
  # Sensory
  "Vision presence of cataracts",
  "Smell preference"
)
                           # levels = c("Overall",
                           #            "Adrenal-Cortical adenoma",
                           #            "Autoshaping learning test",
                           #            "balance on a dowel",
                           #            "Barnes maze test",
                           #            "cardiac fibrosis",
                           #            "Cardiac pathology",
                           #            "Cardiomyocyte size",
                           #            "E/A ratio",
                           #            "Ejection fraction",
                           #            "Energy expenditure",
                           #            "fractional shortening",
                           #            "Frailty score",
                           #            "Glucose tolerance",
                           #            "Grip strength",
                           #            "Harderian adenoma",
                           #            "Heart size",
                           #            "inhibitory avoidance test",
                           #            "Insulin sensitivity",
                           #            "Kidney pathology",
                           #            "left ventricle size",
                           #            "Left Ventricular Isovolumic Relaxation Time",
                           #            "Liver pathology",
                           #            "Mammary pathology",
                           #            "Morris water maze",
                           #            "nonthymic lymphosarcoma",
                           #            "novel object recognition",
                           #            "Open field test",
                           #            "pituitary tumors",
                           #            "Polyarteritis",
                           #            "Proportion with hypophyseal adenoma",
                           #            "Proportion with tumors ",
                           #            "Pulmonary adenoma",
                           #            "Quadriceps size",
                           #            "RAM memory test",
                           #            "Reticulum cell sarcoma",
                           #            "rotarod",
                           #            "Skeletal muscle fiber size",
                           #            "Smell preference",
                           #            "superficial skin and subcutaneous tumor not mammary",
                           #            "T cell function test",
                           #            "T maze test",
                           #            "thymic lymphomas",
                           #            "Total activity dark period",
                           #            "total non-neoplastic lesions",
                           #            "Treamill",
                           #            "vision presence of cataracts",
                           #            "voluntary activity assessment",
                           #            "voluntary wheel running",
                           #            "water radial arm maze test",
                           #            "Y maze testing"),
                           #    labels = c("Overall",
                           #               "Adrenal-cortical adenoma",
                           #               "Autoshaping learning test",
                           #               "Balance on a dowel",
                           #               "Barnes maze test",
                           #               "Cardiac fibrosis",
                           #               "Cardiac pathology",
                           #               "Cardiomyocyte size",
                           #               "E/A ratio",
                           #               "Ejection fraction",
                           #               "Energy expenditure",
                           #               "Fractional shortening",
                           #               "Frailty score",
                           #               "Glucose tolerance",
                           #               "Grip strength",
                           #               "Harderian adenoma",
                           #               "Heart size",
                           #               "Inhibitory avoidance test",
                           #               "Insulin sensitivity",
                           #               "Kidney pathology",
                           #               "left ventricle size",
                           #               "Left ventricular isovolumic relaxation time",
                           #               "Liver pathology",
                           #               "Mammary pathology",
                           #               "Morris water maze",
                           #               "Nonthymic lymphosarcoma",
                           #               "Novel object recognition",
                           #               "Open field test",
                           #               "Pituitary tumors",
                           #               "Polyarteritis",
                           #               "Proportion with hypophyseal adenoma",
                           #               "Proportion with tumors",
                           #               "Pulmonary adenoma",
                           #               "Quadriceps size",
                           #               "RAM memory test",
                           #               "Reticulum cell sarcoma",
                           #               "Rotarod",
                           #               "Skeletal muscle fiber size",
                           #               "Smell preference",
                           #               "Superficial skin and subcutaneous tumor not mammary",
                           #               "T cell function test",
                           #               "T maze test",
                           #               "Thymic lymphomas",
                           #               "Total activity dark period",
                           #               "Total non-neoplastic lesions",
                           #               "Treamill",
                           #               "Vision presence of cataracts",
                           #               "Voluntary activity assessment",
                           #               "voluntary wheel running",
                           #               "Water radial arm maze test",
                           #               "Y maze testing")
                           )

mestype_sex <- ggplot(data = ldat, aes(x = yi, y = Measurement.type)) +
  geom_errorbarh(aes(xmin = lower.ci, xmax = upper.ci, colour = Sex), 
                 height = 0, show.legend = TRUE, linewidth = 2, 
                 alpha = 0.8, position =position_dodge(width = 0.75)) +
  geom_point(aes(col = Sex), fill = "white", size = 2, shape = 21, position =position_dodge2(width = 0.75)) +
  geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
  geom_vline(xintercept = res$estimate, linetype = 1, colour = "red", alpha = 0.3) +
  xlim(-4, 3) +
  #creating 95% prediction intervals
  geom_segment(data = res, ggplot2::aes(x = lowerPR, y = 1, xend = upperPR, yend = 1, group = name)) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data, ggplot2::aes(x = x.diamond, y = y.diamond), fill = "red") +
  
  theme_bw() +
  scale_color_manual(values = c("#CC6677", "#88CCEE")) +
  labs(x = "lnRR (effect size)", y = "", colour = "Sex") +
  theme(legend.position = c(0.95, 0.85),
        legend.justification = c(1, 0)) +
  theme(legend.title = element_text(size = 9)) +
  #theme(legend.direction="horizontal") +
  theme(axis.text.y = element_blank()) +
  theme(axis.text.y = element_text(size = 10, colour ="black",
                                   hjust = 0.5)) 

mestype_sex

# icons 

filenames <- list.files(here("icons", "literature"), pattern=".png", full.names=TRUE)
ldf <- lapply(filenames, readPNG)
names(ldf) <- substr(filenames, 99, 99+60)

mestype_sex1 <- mestype_sex +
  annotation_custom(rasterGrob(ldf$Mus_musculus.png), xmin = -1.5*2.5, xmax = -1*2.5, ymin = 13.5*3.5, ymax = 14.5*3.5) +
  annotation_custom(rasterGrob(ldf$Rattus_argentiventer.png), xmin = -1*2.5, xmax = -0.5*2.5, ymin = 13*3.5, ymax = 14*3.5)

mestype_sex1

17 Publication bais & sensitivity analysis

Funnel plot

# raw funnel plot
# funnel plot - 
funnel(mod)

funnel(mod3)

Small-study effect: uni-moderaotor

dat$Effective_N <- 1/dat$Sample_size_experimental + 1/dat$Sample_size_control

egger_hs <-  rma.mv(yi = yi, 
                 V = vi, 
                 mod = ~sqrt(Effective_N), 
                 random = list(~ 1|Strain, 
                               ~ 1|Study, 
                               ~ 1|Effect_ID), 
                 data = dat, 
                 test = "t",
                 sparse = TRUE,
                 control=list(optimizer="optim", optmethod="BFGS")
)

summary(egger_hs)

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-140.8768   281.7537   291.7537   308.0412   292.0763   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0242  0.1557     23     no     Strain 
sigma^2.2  0.0000  0.0011     48     no      Study 
sigma^2.3  0.0603  0.2456    194     no  Effect_ID 

Test for Residual Heterogeneity:
QE(df = 192) = 344.7464, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 192) = 0.6690, p-val = 0.4144

Model Results:

                   estimate      se     tval   df    pval    ci.lb   ci.ub    
intrcpt              0.1636  0.1072   1.5255  192  0.1288  -0.0479  0.3751    
sqrt(Effective_N)   -0.2555  0.3124  -0.8179  192  0.4144  -0.8717  0.3607    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Decline effect (time lag bias): uni-moderator

dat$Year <- as.numeric(str_extract(as.character(dat$Study),"[:digit:][:digit:][:digit:][:digit:]"))

decline <-  rma.mv(yi = yi, 
                 V = vi, 
                 mod = ~ Year, 
                 random = list(~ 1|Strain, 
                               ~ 1|Study, 
                               ~ 1|Effect_ID), 
                 data = dat, 
                 test = "t",
                 sparse = TRUE,
                 control=list(optimizer="optim", optmethod="BFGS")
)

summary(decline)

Multivariate Meta-Analysis Model (k = 194; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
-140.0392   280.0784   290.0784   306.3659   290.4010   

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0220  0.1484     23     no     Strain 
sigma^2.2  0.0000  0.0011     48     no      Study 
sigma^2.3  0.0585  0.2418    194     no  Effect_ID 

Test for Residual Heterogeneity:
QE(df = 192) = 328.9033, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 192) = 2.7164, p-val = 0.1010

Model Results:

         estimate      se     tval   df    pval    ci.lb    ci.ub    
intrcpt    6.8728  4.1175   1.6692  192  0.0967  -1.2485  14.9941  . 
Year      -0.0034  0.0021  -1.6482  192  0.1010  -0.0075   0.0007    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Leave-one-study-out analysis

# The function for leave-one-study-out

dat$Study <- as.factor(dat$Study)

LeaveOneOut_effectsize <- list()
for(i in 1:length(levels(dat$Study))){
  dat1 <- dat[dat$Study != levels(dat$Study)[i], ]
  
  LeaveOneOut_effectsize[[i]] <- rma.mv(yi = yi, 
                 V = vi, 
                 random = list(~ 1|Strain, 
                               ~ 1|Study, 
                               ~ 1|Effect_ID), 
                 data = dat1, 
                 test = "t",
                 sparse = TRUE,
                 control=list(optimizer="optim", optmethod="BFGS")
  )
                 
}


# writing function for extracting est, ci.lb, and ci.ub from all models
est.func <- function(mod){
  df <- data.frame(est = mod$b, lower = mod$ci.lb, upper = mod$ci.ub)
  return(df)
}

#using dplyr to form data frame
MA_LOO <- lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>% mutate(left_out = levels(dat$Study))


saveRDS(MA_LOO,file = here("Rdata", "literature","MA_LOO2.rds"))

#telling ggplot to stop reordering factors
MA_LOO <- readRDS(file = here("Rdata",  "literature", "MA_LOO2.rds"))

MA_LOO$left_out<- as.factor(MA_LOO$left_out)
MA_LOO$left_out<-factor(MA_LOO$left_out, levels = MA_LOO$left_out)


#plotting
leaveoneout_E <- ggplot(MA_LOO) +
  geom_hline(yintercept = 0, lty = 2, lwd = 1) +
  geom_hline(yintercept = mod$ci.lb, lty = 3, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod$b, lty = 1, lwd = 0.75, colour = "black") +
  geom_hline(yintercept = mod$ci.ub, lty = 3, lwd = 0.75, colour = "black") +
  geom_pointrange(aes(x = left_out, y = est, ymin = lower, ymax = upper)) +
  xlab("Study left out") + 
  ylab("lnRR, 95% CI") + 
  coord_flip() +
  theme(panel.grid.minor = element_blank())+
  theme_bw() + theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor.x = element_blank() ) +
  theme(axis.text.y = element_text(size = 6))

leaveoneout_E

18 PART III: LIFESPAN ANALYSIS ON ZOO DATA

19 Data preparation & processing

Loading data

# main data
dat0 <- read_csv(here("data", "zoo", "resultsBaSTApost2005oldFormatFeb2025.csv"), na = c("", "NA"), show_col_types = FALSE)

# phylogeny
load(here("Rdata", "zoo", "maxCredTree.RData"))

tree <- maxCred 

# taxonomy
tax <- read.csv(here("data", "zoo", "FinalTranslTab.csv"))

dat0 %>% left_join(tax, by = c("species" = "ZIMSspecies")) -> dat_full

# talking out species with no data (Pseudocheirus peregrinus = likely to be mistaks in data)
dat_full %>% 
  # filter(
  # species != "Chrysocyon brachyurus" &
  #                   species != "Crocuta crocuta" &
  #                   species != "Neofelis nebulosa" &
  #                   species != "Panthera uncia" &
  #                   species != "Pseudocheirus peregrinus") %>% 
  mutate(phylogeny = gsub(" ", "_", vertlifeSpecies)) -> dat

# adding Cervus canadensis
# dat$vertlifeSpecies[which(dat$species == "Cervus canadensis")] <-"Cervus canadensis"
# dat$phylogeny[which(dat$species == "Cervus canadensis")] <-"Cervus_canadensis"

# fixing species name
#dat$species[dat$species == "Equus asinus"] <- "Equus_africanus"
dat$species[dat$species == "Aonyx cinereus"] <- "Aonyx cinerea"
#dat$species[dat$species == "Bubalus bubalis"] <- "Bubalus arnee"


# life span data 
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% unique(dat$phylogeny)))]

tree <- drop.tip(tree, to_drop)

# checking the number of spp
#length(tree$tip.label)
tree <- as.ultrametric(tree)

#tree <- compute.brlen(tree)
cor_tree <- vcv(tree, corr = TRUE)

Preparing data set (contraception vs. normal)

##############################################
# data sets comparing normal vs. contraception 
##############################################

# male surgical

dat_m_surg <- dat %>% filter(is.na(Male_Surgical_Contra_Mean) == FALSE, is.na(Male_Surgical_NoContra_Mean) == FALSE) %>% 
  mutate(M_control_m = Male_Surgical_NoContra_Mean,
         M_control_sd = sqrt(Male_Surgical_NoContra_N)*Male_Surgical_NoContra_SE,
         M_control_n = Male_Surgical_NoContra_N,
         M_surgical_m = Male_Surgical_Contra_Mean,
         M_surgical_sd = sqrt(Male_Surgical_Contra_N)*Male_Surgical_Contra_SE,
         M_surgical_n = Male_Surgical_Contra_N,
         sex = "male",
         type = "surgical")

dat_m_surg <- escalc("ROM", 
              m1i = M_surgical_m,
              m2i = M_control_m,
              sd1i = M_surgical_sd,
              sd2i = M_control_sd,
              n1i = M_surgical_n,
              n2i = M_control_n,
              data = dat_m_surg,
              )

#  male hormonal
dat_m_horm <- dat %>% filter(is.na(Male_Hormonal_Contra_Mean) == FALSE, is.na(Male_Hormonal_NoContra_Mean) == FALSE) %>% 
  mutate(M_control_m = Male_Hormonal_NoContra_Mean,
         M_control_sd = sqrt(Male_Hormonal_NoContra_N)*Male_Hormonal_NoContra_SE,
         M_control_n =Male_Hormonal_NoContra_N,
         M_hormonal_m = Male_Hormonal_Contra_Mean,
         M_hormonal_sd = sqrt(Male_Hormonal_Contra_SE)*Male_Hormonal_Contra_N,
         M_hormonal_n = Male_Hormonal_Contra_N,
         sex = "male",
         type = "hormonal")

dat_m_horm <- escalc("ROM", 
                     m1i = M_hormonal_m,
                     m2i = M_control_m,
                     sd1i = M_hormonal_sd,
                     sd2i = M_control_sd,
                     n1i = M_hormonal_n,
                     n2i = M_control_n,
                     data = dat_m_horm,
)


# female hormonal data 

dat_f_horm <- dat %>% filter(is.na(Female_Hormonal_Contra_Mean) == FALSE, is.na(Female_Hormonal_NoContra_Mean) == FALSE) %>% 
  mutate(F_control_m = Female_Hormonal_NoContra_Mean,
         F_control_sd = sqrt(Female_Hormonal_NoContra_N)*Female_Hormonal_NoContra_SE,
         F_control_n = Female_Hormonal_NoContra_N,
         F_hormonal_m = Female_Hormonal_Contra_Mean,
         F_hormonal_sd = sqrt(Female_Hormonal_Contra_N)*Female_Hormonal_Contra_SE,
         F_hormonal_n = Female_Hormonal_Contra_N,
         sex = "female",
         type = "hormonal")

dat_f_horm <- escalc("ROM", 
                     m1i = F_hormonal_m,
                     m2i = F_control_m,
                     sd1i = F_hormonal_sd,
                     sd2i = F_control_sd,
                     n1i = F_hormonal_n,
                     n2i = F_control_n,
                     data = dat_f_horm,
)

# female surgical data

dat_f_surg<- dat %>% filter(is.na(Female_Surgical_Contra_Mean) == FALSE, is.na(Female_Hormonal_NoContra_Mean) == FALSE) %>% 
  mutate(F_control_m = Female_Surgical_NoContra_Mean,
         F_control_sd = sqrt(Female_Surgical_NoContra_N)*Female_Surgical_NoContra_SE,
         F_control_n = Female_Surgical_NoContra_N,
         F_surgical_m = Female_Surgical_Contra_Mean,
         F_surgical_sd = sqrt(Female_Surgical_Contra_N)*Female_Surgical_Contra_SE,
         F_surgical_n = Female_Surgical_Contra_N,
         sex = "female",
         type = "surgical")


dat_f_surg <- escalc("ROM", 
                     m1i = F_surgical_m,
                     m2i = F_control_m,
                     sd1i = F_surgical_sd,
                     sd2i = F_control_sd,
                     n1i = F_surgical_n,
                     n2i = F_control_n,
                     data = dat_f_surg,
)

rbind(
dat_m_horm[ , c(1, 27, 28, 35:38)], # 1
dat_m_surg[ ,c(1, 27, 28, 35:38)], # 2
dat_f_horm[ , c(1, 27, 28, 35:38)], # 3 
dat_f_surg[ ,c(1, 27, 28, 35:38)] # 4
) -> dat_all

# observation id
dat_all$obs_id <- factor(1:nrow(dat_all))

# sex type combined
dat_all %>% mutate(sex_type = paste(sex, type, sep = "_")) -> dat_all

#write_csv(dat_all, here("data", "zoo", "effect1.csv"))

Preparing data set (sex differences)

######################################
# data sets looking at sex differences
######################################

# female normal vs. male surgical (1)

dat_fm_ns <- dat %>% filter(is.na(Female_Surgical_NoContra_Mean) == FALSE, 
                            is.na(Male_Surgical_Contra_Mean) == FALSE) %>% 
  transmute(F_normal_m = Female_Surgical_NoContra_Mean,
         F_normal_sd = sqrt(Female_Surgical_NoContra_N)*Female_Surgical_NoContra_SE,
         F_normal_n = Female_Surgical_NoContra_N,
         M_surgical_m = Male_Surgical_Contra_Mean,
         M_surgical_sd = sqrt(Male_Surgical_Contra_N)*Male_Surgical_Contra_SE,
         M_surgical_n = Male_Surgical_Contra_N,
         species = species,
         phylogeny = phylogeny,
         category = "M normal/F surgical"
         )

dat_fm_ns <- escalc("ROM", 
              m2i = F_normal_m,
              m1i = M_surgical_m,
              sd2i = F_normal_sd,
              sd1i = M_surgical_sd,
              n2i = F_normal_n,
              n1i = M_surgical_n,
              data = dat_fm_ns,
              )


# female hormonal vs. male normal (2)

dat_fm_hn <- dat %>% filter(is.na(Female_Hormonal_Contra_Mean) == FALSE, 
                            is.na(Male_Hormonal_NoContra_Mean) == FALSE) %>% 
  transmute(F_hormonal_m = Female_Hormonal_Contra_Mean,
         F_hormonal_sd = sqrt(Female_Hormonal_Contra_N)*Female_Hormonal_Contra_SE,
         F_hormonal_n = Female_Hormonal_Contra_N,
         M_normal_m = Male_Hormonal_NoContra_Mean,
         M_normal_sd = sqrt(Male_Hormonal_NoContra_N)*Male_Hormonal_NoContra_SE,
         M_normal_n = Male_Hormonal_NoContra_N,
         species = species,
         phylogeny = phylogeny,
         category = "M hormonal/F normal"
         )

dat_fm_hn <- escalc("ROM", 
              m2i = F_hormonal_m,
              m1i = M_normal_m,
              sd2i = F_hormonal_sd,
              sd1i = M_normal_sd,
              n2i = F_hormonal_n,
              n1i = M_normal_m,
              data = dat_fm_hn,
              )



# female surgical vs. male normal (3)

dat_fm_sn <- dat %>% filter(is.na(Female_Surgical_Contra_Mean) == FALSE,
                            is.na(Male_Surgical_NoContra_Mean) == FALSE) %>%
  transmute(F_surgical_m = Female_Surgical_Contra_Mean,
         F_surgical_sd = sqrt(Female_Surgical_Contra_N)*Female_Surgical_Contra_SE,
         F_surgical_n = Female_Surgical_Contra_N,
         M_normal_m = Male_Surgical_NoContra_Mean,
         M_normal_sd = sqrt(Male_Surgical_NoContra_N)*Male_Surgical_NoContra_SE,
         M_normal_n = Male_Surgical_NoContra_N,
         species = species,
         phylogeny = phylogeny,
         category = "M hormonal/F normal"
         )

dat_fm_sn <- escalc("ROM", 
              m2i = F_surgical_m,
              m1i = M_normal_m,
              sd2i = F_surgical_sd,
              sd1i = M_normal_sd,
              n2i = F_surgical_n,
              n1i = M_normal_n,
              data = dat_fm_sn,
              )


# female surgical vs. male surgical (4)

dat_fm_ss <- dat %>% filter(is.na(Female_Surgical_Contra_Mean) == FALSE, 
                            is.na(Male_Surgical_Contra_Mean) == FALSE) %>% 
    transmute(F_surgical_m = Female_Surgical_Contra_Mean,
                 F_surgical_sd = sqrt(Female_Surgical_Contra_N)*Female_Surgical_Contra_SE,
                 F_surgical_n = Female_Surgical_Contra_N,
                 M_surgical_m = Male_Surgical_Contra_Mean,
                 M_surgical_sd = sqrt(Male_Surgical_Contra_N)*Male_Surgical_Contra_SE,
                 M_surgical_n = Male_Surgical_Contra_N,
                 species = species,
                 phylogeny = phylogeny,
                 category = "M surgical/F surgical"
                 )

dat_fm_ss <- escalc("ROM", 
                            m2i = F_surgical_m,
                            m1i = M_surgical_m,
                            sd2i = F_surgical_sd,
                            sd1i = M_surgical_sd,
                            n2i = F_surgical_n,
                            n1i = M_surgical_n,
                            data = dat_fm_ss,
                            )

# female normal vs. male normal (5)

dat_fm_nn1 <- dat %>% filter(is.na(Female_Hormonal_NoContra_Mean) == FALSE, 
                                                        is.na(Male_Hormonal_NoContra_Mean) == FALSE) %>% 
    transmute(F_normal_m = Female_Hormonal_NoContra_Mean,
                 F_normal_sd = sqrt(Female_Hormonal_NoContra_N)*Female_Hormonal_NoContra_SE,
                 F_normal_n = Female_Hormonal_NoContra_N,
                 M_normal_m = Male_Hormonal_NoContra_Mean,
                 M_normal_sd = sqrt(Male_Hormonal_NoContra_N)*Male_Hormonal_NoContra_SE,
                 M_normal_n = Male_Hormonal_NoContra_N,
                 species = species,
                 phylogeny = phylogeny,
                 category = "M normal/F normal"
                 )

dat_fm_nn1 <- escalc("ROM", 
                            m2i = F_normal_m,
                            m1i = M_normal_m,
                            sd2i = F_normal_sd,
                            sd1i = M_normal_sd,
                            n2i = F_normal_n,
                            n1i = M_normal_n,
                            data = dat_fm_nn1,
                            )

dat_fm_nn2 <- dat %>% filter(is.na(Female_Surgical_NoContra_Mean) == FALSE, 
                                                        is.na(Male_Surgical_NoContra_Mean) == FALSE) %>% 
    transmute(F_normal_m = Female_Surgical_NoContra_Mean,
                 F_normal_sd = sqrt(Female_Surgical_NoContra_N)*Female_Surgical_NoContra_SE,
                 F_normal_n = Female_Surgical_NoContra_N,
                 M_normal_m = Male_Surgical_NoContra_Mean,
                 M_normal_sd = sqrt(Male_Surgical_NoContra_N)*Male_Surgical_NoContra_SE,
                 M_normal_n = Male_Surgical_NoContra_N,
                 species = species,
                 phylogeny = phylogeny,
                 category = "M normal/F normal"
                 )

dat_fm_nn2 <- escalc("ROM", 
                            m2i = F_normal_m,
                            m1i = M_normal_m,
                            sd2i = F_normal_sd,
                            sd1i = M_normal_sd,
                            n2i = F_normal_n,
                            n1i = M_normal_n,
                            data = dat_fm_nn2,
                            )



# female normal vs. male hormonal (6)

dat_fm_nh <- dat %>% filter(is.na(Female_Hormonal_NoContra_Mean) == FALSE,
                            is.na(Male_Hormonal_Contra_Mean) == FALSE) %>%
  transmute(F_normal_m = Female_Hormonal_NoContra_Mean,
            F_normal_sd = sqrt(Female_Hormonal_NoContra_N)*Female_Hormonal_NoContra_SE,
            F_normal_n = Female_Hormonal_NoContra_N,
            M_hornomal_m = Male_Hormonal_Contra_Mean,
            M_hornomal_sd = sqrt(Male_Hormonal_Contra_N)*Male_Hormonal_Contra_SE,
            M_hornomal_n = Male_Hormonal_Contra_N,
            species = species,
            phylogeny = phylogeny,
            category = "M normal/F hornomal"
  )

dat_fm_nh <- escalc("ROM", 
                    m2i = F_normal_m,
                    m1i = M_hornomal_m,
                    sd2i = F_normal_sd,
                    sd1i = M_hornomal_sd,
                    n2i = F_normal_n,
                    n1i = M_hornomal_n,
                    data = dat_fm_nh,
)


# female hormonal vs. male hormonal (9)

dat_fm_hh <- dat %>% filter(is.na(Female_Hormonal_Contra_Mean) == FALSE,
                            is.na(Male_Hormonal_Contra_Mean) == FALSE) %>%
  transmute(F_hornomal_m = Female_Hormonal_Contra_Mean,
            F_hornomal_sd = sqrt(Female_Hormonal_Contra_N)*Female_Hormonal_Contra_SE,
            F_hornomal_n = Female_Hormonal_Contra_N,
            M_hornomal_m = Male_Hormonal_Contra_Mean,
            M_hornomal_sd = sqrt(Male_Hormonal_Contra_N)*Male_Hormonal_Contra_SE,
            M_hornomal_n = Male_Hormonal_Contra_N,
            species = species,
            phylogeny = phylogeny,
            category = "M hornomal/F hornomal"
  )
     
dat_fm_hh <- escalc("ROM", 
                    m2i = F_hornomal_m,
                    m1i = M_hornomal_m,
                    sd2i = F_hornomal_sd,
                    sd1i = M_hornomal_sd,
                    n2i = F_hornomal_n,
                    n1i = M_hornomal_n,
                    data = dat_fm_hh,
)



bind_rows(
  dat_fm_ns[ , 7:11], # 1
  dat_fm_hn[ , 7:11], # 2 
  dat_fm_sn[ , 7:11], # 3
  dat_fm_ss[ , 7:11], # 4
  dat_fm_nn1[ , 7:11], # 5
  dat_fm_nn2[ , 7:11], # 5
  dat_fm_nh[ , 7:11], # 6
  dat_fm_hh[ , 7:11], # 9
) -> dat_comb


dat_comb$obs_id <- factor(1:nrow(dat_comb))

# adding category
dat_comb$contraception <- factor(dat_comb$category, levels = c(
                                                           "M hornomal/F hornomal",
                                                           "M surgical/F surgical",
                                                           "M hormonal/F normal",  
                                                           "M surgical/F normal",
                                                           "M normal/F hormonal",
                                                           "M normal/F surgical",
                                                           "M normal/F normal"),
                                                labels = c(
                                                           "Male sterlized/\nFemale sterlized",
                                                           "Male sterlized/\nFemale sterlized",
                                                           "Male sterlized/\nFemale normal",
                                                           "Male sterlized/\nFemale normal",  
                                                           "Male normal/\nFemale sterlized",
                                                           "Male normal/\nFemale sterlized",
                                                           "Male normal/\nFemale normal"),
                             
)

# absolute values
dat_comb <- dat_comb %>% mutate(
  abs_yi2 = abs(yi), # conservative
  abs_yi = folded_mu(yi, vi), # alternative way
  abs_vi = folded_v(yi, vi))

#write_csv(dat_comb, here("data", "zoo", "effect2.csv"))

20 Datasets

Original dataset (used)

kable(dat, "html", escape = FALSE, digits = 3) %>% kable_styling("striped", position = "left", full_width = TRUE) %>% 
  scroll_box(width = "100%", 
    height = "500px")

species	Female_Hormonal_Contra_N	Female_Hormonal_Contra_Mean	Female_Hormonal_Contra_SE	Female_Hormonal_NoContra_N	Female_Hormonal_NoContra_Mean	Female_Hormonal_NoContra_SE	Female_Surgical_Contra_N	Female_Surgical_Contra_Mean	Female_Surgical_Contra_SE	Female_Surgical_NoContra_N	Female_Surgical_NoContra_Mean	Female_Surgical_NoContra_SE	Male_Hormonal_Contra_N	Male_Hormonal_Contra_Mean	Male_Hormonal_Contra_SE	Male_Hormonal_NoContra_N	Male_Hormonal_NoContra_Mean	Male_Hormonal_NoContra_SE	Male_Surgical_Contra_N	Male_Surgical_Contra_Mean	Male_Surgical_Contra_SE	Male_Surgical_NoContra_N	Male_Surgical_NoContra_Mean	Male_Surgical_NoContra_SE	vertlifeSpecies	order	phylogeny
Addax nasomaculatus	37	9.404	0.885	935	8.661	0.189	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	82	8.783	0.705	844	7.717	0.255	Addax nasomaculatus	Artiodactyla	Addax_nasomaculatus
Aepyceros melampus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	120	7.286	0.638	485	4.430	0.218	Aepyceros melampus	Artiodactyla	Aepyceros_melampus
Alouatta caraya	93	17.460	1.367	111	13.204	1.055	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	38	14.720	1.599	227	13.845	0.660	Alouatta caraya	Primates	Alouatta_caraya
Ammotragus lervia	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	131	9.735	0.574	2019	10.299	0.197	Ammotragus lervia	Artiodactyla	Ammotragus_lervia
Antilope cervicapra	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	290	8.130	0.485	2336	4.937	0.115	Antilope cervicapra	Artiodactyla	Antilope_cervicapra
Aonyx cinerea	113	10.956	0.641	1307	9.375	0.190	NA	NA	NA	NA	NA	NA	42	7.857	0.771	545	6.884	0.201	93	10.099	0.678	1355	10.022	0.209	Aonyx cinerea	Carnivora	Aonyx_cinerea
Atelerix albiventris	NA	NA	NA	NA	NA	NA	30	2.480	0.304	984	3.017	0.065	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Atelerix albiventris	Eulipotyphla	Atelerix_albiventris
Ateles geoffroyi	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	36	19.667	3.370	157	25.520	2.487	Ateles geoffroyi	Primates	Ateles_geoffroyi
Axis axis	37	5.867	0.968	634	9.842	0.907	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Axis axis	Artiodactyla	Axis_axis
Bison bison	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	83	12.235	0.953	624	10.625	0.406	Bison bison	Artiodactyla	Bison_bison
Bos grunniens	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	32	8.477	1.353	325	10.341	0.493	Bos grunniens	Artiodactyla	Bos_grunniens
Boselaphus tragocamelus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	59	6.968	1.181	563	7.684	0.457	Boselaphus tragocamelus	Artiodactyla	Boselaphus_tragocamelus
Callimico goeldii	54	9.590	1.030	498	8.471	0.298	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Callimico goeldii	Primates	Callimico_goeldii
Callithrix geoffroyi	70	5.920	0.638	537	6.464	0.250	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Callithrix geoffroyi	Primates	Callithrix_geoffroyi
Callithrix jacchus	126	7.104	0.628	1458	6.688	0.175	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	125	9.068	0.821	1702	7.248	0.177	Callithrix jacchus	Primates	Callithrix_jacchus
Camelus bactrianus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	80	12.456	1.013	587	13.328	0.380	Camelus bactrianus	Artiodactyla	Camelus_bactrianus
Camelus dromedarius	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	72	13.010	1.334	443	15.676	0.576	Camelus dromedarius	Artiodactyla	Camelus_dromedarius
Canis latrans	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	51	11.551	0.617	150	10.160	0.447	Canis latrans	Carnivora	Canis_latrans
Canis lupus	NA	NA	NA	NA	NA	NA	92	8.770	0.499	405	8.026	0.244	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Canis lupus	Carnivora	Canis_lupus
Capra hircus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	503	8.020	0.219	1858	8.250	0.132	Capra hircus	Artiodactyla	Capra_hircus
Cavia porcellus	NA	NA	NA	NA	NA	NA	42	3.072	0.276	3222	3.841	0.045	NA	NA	NA	NA	NA	NA	144	3.848	0.211	1379	3.713	0.070	Cavia porcellus	Rodentia	Cavia_porcellus
Cebuella pygmaea	47	6.839	0.887	961	5.652	0.158	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Cebuella pygmaea	Primates	Cebuella_pygmaea
Cebus capucinus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	43	27.580	2.778	131	19.896	1.558	Cebus capucinus	Primates	Cebus_capucinus
Cercopithecus neglectus	57	16.193	1.434	193	16.006	0.801	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Cercopithecus neglectus	Primates	Cercopithecus_neglectus
Cervus elaphus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	40	8.131	1.147	491	7.331	0.416	Cervus elaphus	Artiodactyla	Cervus_elaphus
Chinchilla lanigera	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	33	8.291	1.314	581	7.222	0.299	Chinchilla lanigera	Rodentia	Chinchilla_lanigera
Colobus guereza	101	17.032	1.278	290	11.754	0.636	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Colobus guereza	Primates	Colobus_guereza
Cynomys ludovicianus	NA	NA	NA	NA	NA	NA	45	5.258	0.717	1277	5.169	0.141	NA	NA	NA	NA	NA	NA	116	4.916	0.431	1108	5.333	0.174	Cynomys ludovicianus	Rodentia	Cynomys_ludovicianus
Dama dama	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	142	9.422	0.981	1922	6.780	0.203	Dama dama	Artiodactyla	Dama_dama
Desmodus rotundus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	69	14.310	1.600	464	11.066	0.505	Desmodus rotundus	Chiroptera	Desmodus_rotundus
Didelphis virginiana	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	54	1.712	0.124	389	1.564	0.054	Didelphis virginiana	Didelphimorphia	Didelphis_virginiana
Dolichotis patagonum	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	107	5.813	0.573	1744	5.303	0.135	Dolichotis patagonum	Rodentia	Dolichotis_patagonum
Eidolon helvum	NA	NA	NA	NA	NA	NA	92	19.701	1.012	548	16.746	0.436	NA	NA	NA	NA	NA	NA	164	15.747	0.756	493	14.047	0.415	Eidolon helvum	Chiroptera	Eidolon_helvum
Equus caballus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	89	18.452	1.440	543	19.652	0.756	Equus caballus	Perissodactyla	Equus_caballus
Equus quagga	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	50	21.485	2.425	235	13.169	0.890	Equus quagga	Perissodactyla	Equus_quagga
Erythrocebus patas	44	13.913	1.353	305	11.944	0.508	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	36	10.223	1.412	233	10.912	0.651	Erythrocebus patas	Primates	Erythrocebus_patas
Eudorcas thomsonii	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	56	7.969	0.849	358	5.150	0.286	Eudorcas thomsonii	Artiodactyla	Eudorcas_thomsonii
Eulemur macaco	44	15.286	2.019	131	11.052	0.831	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Eulemur macaco	Primates	Eulemur_macaco
Felis catus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	76	12.748	1.414	476	12.373	0.691	Felis catus	Carnivora	Felis_catus
Giraffa camelopardalis	48	16.456	1.622	362	11.278	0.437	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Giraffa camelopardalis	Artiodactyla	Giraffa_camelopardalis
Hemitragus jemlahicus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	36	7.154	0.987	298	7.437	0.409	Hemitragus jemlahicus	Artiodactyla	Hemitragus_jemlahicus
Hydrochoerus hydrochaeris	41	5.711	0.597	1867	6.094	0.108	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	121	5.491	0.382	1513	5.593	0.123	Hydrochoerus hydrochaeris	Rodentia	Hydrochoerus_hydrochaeris
Kobus megaceros	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	70	7.206	0.764	332	6.259	0.328	Kobus megaceros	Artiodactyla	Kobus_megaceros
Lama glama	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	219	13.010	0.782	1320	14.044	0.374	Lama glama	Artiodactyla	Lama_glama
Lama guanicoe	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	52	11.687	1.608	346	11.654	0.643	Lama guanicoe	Artiodactyla	Lama_guanicoe
Lemur catta	485	16.442	0.682	3377	14.746	0.225	61	14.881	1.478	3810	15.029	0.220	NA	NA	NA	NA	NA	NA	429	17.304	0.826	3789	15.697	0.228	Lemur catta	Primates	Lemur_catta
Leontopithecus chrysomelas	87	10.635	0.775	346	8.604	0.354	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Leontopithecus chrysomelas	Primates	Leontopithecus_chrysomelas
Leontopithecus rosalia	164	10.250	0.535	280	8.353	0.379	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Leontopithecus rosalia	Primates	Leontopithecus_rosalia
Leptailurus serval	39	11.373	1.386	516	12.374	0.417	38	14.381	1.441	516	12.352	0.423	NA	NA	NA	NA	NA	NA	62	13.596	0.772	439	11.363	0.348	Leptailurus serval	Carnivora	Leptailurus_serval
Lycaon pictus	79	7.472	0.541	328	7.031	0.242	74	8.274	0.540	328	7.078	0.241	30	5.053	0.512	312	5.141	0.152	NA	NA	NA	NA	NA	NA	Lycaon pictus	Carnivora	Lycaon_pictus
Lynx lynx	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	23	8.151	0.815	154	7.514	0.367	Lynx lynx	Carnivora	Lynx_lynx
Lynx rufus	NA	NA	NA	NA	NA	NA	44	12.164	0.843	188	12.299	0.453	NA	NA	NA	NA	NA	NA	76	16.504	0.774	233	14.127	0.540	Lynx rufus	Carnivora	Lynx_rufus
Macaca fascicularis	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	71	13.550	2.437	362	11.692	0.859	Macaca fascicularis	Primates	Macaca_fascicularis
Macaca fuscata	82	16.090	1.045	398	17.007	0.586	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	95	15.961	1.389	261	14.113	0.801	Macaca fuscata	Primates	Macaca_fuscata
Macaca leonina	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	46	11.400	2.742	60	14.702	2.922	Macaca leonina	Primates	Macaca_leonina
Macaca mulatta	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	119	21.963	1.852	340	16.382	0.903	Macaca mulatta	Primates	Macaca_mulatta
Macaca nemestrina	30	12.476	2.058	177	13.511	0.928	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Macaca nemestrina	Primates	Macaca_nemestrina
Macaca nigra	73	13.287	1.207	158	13.483	0.832	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	58	8.971	1.120	150	10.869	0.744	Macaca nigra	Primates	Macaca_nigra
Macaca silenus	80	17.397	1.688	305	16.555	0.740	NA	NA	NA	NA	NA	NA	37	13.802	2.074	273	13.029	0.676	46	13.358	1.534	263	12.543	0.688	Macaca silenus	Primates	Macaca_silenus
Macaca sylvanus	237	15.123	0.834	494	12.926	0.517	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	235	16.668	1.254	389	11.496	0.577	Macaca sylvanus	Primates	Macaca_sylvanus
Macropus fuliginosus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	89	7.698	0.781	389	6.289	0.350	Macropus fuliginosus	Diprotodontia	Macropus_fuliginosus
Macropus giganteus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	91	7.935	0.605	425	6.567	0.307	Macropus giganteus	Diprotodontia	Macropus_giganteus
Mandrillus sphinx	207	18.095	0.850	375	15.310	0.619	NA	NA	NA	NA	NA	NA	38	12.095	1.616	370	12.523	0.570	81	13.610	1.090	393	13.195	0.556	Mandrillus sphinx	Primates	Mandrillus_sphinx
Mephitis mephitis	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	113	4.896	0.232	375	5.097	0.135	Mephitis mephitis	Carnivora	Mephitis_mephitis
Mungos mungo	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	8.018	1.048	710	9.392	0.279	Mungos mungo	Carnivora	Mungos_mungo
Muntiacus reevesi	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	64	7.482	0.783	299	5.918	0.276	Muntiacus reevesi	Artiodactyla	Muntiacus_reevesi
Mustela nigripes	NA	NA	NA	NA	NA	NA	40	2.243	0.171	203	2.190	0.093	NA	NA	NA	NA	NA	NA	52	2.664	0.170	423	2.613	0.077	Mustela nigripes	Carnivora	Mustela_nigripes
Myocastor coypus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	38	2.822	0.460	221	2.896	0.203	Myocastor coypus	Rodentia	Myocastor_coypus
Nasua narica	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	34	7.485	0.699	122	8.583	0.472	Nasua narica	Carnivora	Nasua_narica
Nasua nasua	39	6.998	0.672	1056	7.133	0.138	41	9.468	0.867	1428	9.820	0.153	NA	NA	NA	NA	NA	NA	224	9.565	0.384	928	8.381	0.186	Nasua nasua	Carnivora	Nasua_nasua
Nomascus leucogenys	50	25.102	2.628	66	25.019	2.242	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Nomascus leucogenys	Primates	Nomascus_leucogenys
Notamacropus parma	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	43	6.032	0.712	748	4.664	0.156	Macropus parma	Diprotodontia	Macropus_parma
Notamacropus rufogriseus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	620	6.392	0.256	2869	5.433	0.106	Macropus rufogriseus	Diprotodontia	Macropus_rufogriseus
Octodon degus	83	5.591	0.439	502	4.202	0.148	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	175	4.120	0.250	363	3.643	0.148	Octodon degus	Rodentia	Octodon_degus
Odocoileus virginianus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	39	11.605	1.666	270	8.005	0.531	Odocoileus virginianus	Artiodactyla	Odocoileus_virginianus
Oryctolagus cuniculus	NA	NA	NA	NA	NA	NA	42	5.759	0.643	493	4.624	0.180	NA	NA	NA	NA	NA	NA	87	5.062	0.420	312	5.153	0.227	Oryctolagus cuniculus	Lagomorpha	Oryctolagus_cuniculus
Oryx dammah	36	11.274	1.139	2407	10.693	0.141	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	172	11.413	0.660	1818	8.982	0.210	Oryx dammah	Artiodactyla	Oryx_dammah
Osphranter rufus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	221	6.925	0.341	1959	5.703	0.120	Macropus rufus	Diprotodontia	Macropus_rufus
Ovis aries	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	58	7.569	0.763	558	7.842	0.295	Ovis aries	Artiodactyla	Ovis_aries
Pan troglodytes	359	32.835	1.385	659	32.157	0.921	49	32.245	2.623	603	28.307	0.915	NA	NA	NA	NA	NA	NA	226	30.202	1.952	430	22.393	0.919	Pan troglodytes	Primates	Pan_troglodytes
Panthera leo	289	13.217	0.413	1307	11.722	0.192	152	12.486	0.455	1307	11.680	0.194	NA	NA	NA	NA	NA	NA	177	13.062	0.474	1311	13.440	0.221	Panthera leo	Carnivora	Panthera_leo
Panthera onca	35	14.605	0.960	374	14.756	0.364	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Panthera onca	Carnivora	Panthera_onca
Panthera tigris	NA	NA	NA	NA	NA	NA	43	12.221	0.764	551	10.931	0.271	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Panthera tigris	Carnivora	Panthera_tigris
Papio hamadryas	370	17.620	0.605	979	13.618	0.353	134	15.495	0.865	828	12.275	0.351	NA	NA	NA	NA	NA	NA	179	14.001	0.864	945	11.762	0.332	Papio hamadryas	Primates	Papio_hamadryas
Papio papio	41	15.151	1.591	188	15.154	0.777	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	49	13.524	1.884	133	14.236	0.851	Papio papio	Primates	Papio_papio
Pecari tajacu	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	77	12.985	1.043	322	11.849	0.499	Pecari tajacu	Artiodactyla	Pecari_tajacu
Petaurus breviceps	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	105	6.668	0.514	848	7.070	0.211	Petaurus breviceps	Diprotodontia	Petaurus_breviceps
Phacochoerus africanus	51	9.591	0.676	230	9.199	0.342	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Phacochoerus africanus	Artiodactyla	Phacochoerus_africanus
Pithecia pithecia	98	14.737	1.545	183	12.017	0.927	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Pithecia pithecia	Primates	Pithecia_pithecia
Pongo abelii	84	34.009	2.407	245	27.654	1.399	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Pongo abelii	Primates	Pongo_abelii
Potamochoerus porcus	49	9.872	0.765	321	10.322	0.328	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	37	12.158	0.940	305	10.221	0.334	Potamochoerus porcus	Artiodactyla	Potamochoerus_porcus
Procavia capensis	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	62	4.941	0.524	372	4.343	0.196	NA	NA	NA	NA	NA	NA	Procavia capensis	Hyracoidea	Procavia_capensis
Procyon lotor	NA	NA	NA	NA	NA	NA	180	11.019	0.571	1111	10.486	0.223	NA	NA	NA	NA	NA	NA	392	11.439	0.432	998	8.962	0.245	Procyon lotor	Carnivora	Procyon_lotor
Pteropus rodricensis	131	12.309	1.035	845	10.060	0.331	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Pteropus rodricensis	Chiroptera	Pteropus_rodricensis
Puma concolor	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	60	9.157	0.502	315	9.149	0.254	Puma concolor	Carnivora	Puma_concolor
Rangifer tarandus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	48	7.381	0.880	649	5.310	0.214	Rangifer tarandus	Artiodactyla	Rangifer_tarandus
Rattus norvegicus	NA	NA	NA	NA	NA	NA	34	1.480	0.189	647	1.292	0.036	NA	NA	NA	NA	NA	NA	42	1.275	0.120	355	1.248	0.045	Rattus norvegicus	Rodentia	Rattus_norvegicus
Rousettus aegyptiacus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	76	7.384	0.648	985	9.052	0.250	Rousettus aegyptiacus	Chiroptera	Rousettus_aegyptiacus
Saguinus bicolor	64	9.738	1.079	81	7.510	0.739	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Saguinus bicolor	Primates	Saguinus_bicolor
Saguinus oedipus	216	10.693	0.627	1254	9.136	0.224	33	8.245	1.198	1254	9.130	0.235	37	9.276	1.212	1257	9.494	0.194	96	10.797	0.871	1540	10.260	0.202	Saguinus oedipus	Primates	Saguinus_oedipus
Saimiri sciureus	74	14.236	1.414	1726	11.625	0.252	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	114	14.186	1.309	1402	9.195	0.254	Saimiri sciureus	Primates	Saimiri_sciureus
Sapajus apella	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	89	18.216	1.890	793	20.521	0.768	Sapajus apella	Primates	Sapajus_apella
Suricata suricatta	263	8.367	0.310	4132	7.623	0.086	67	6.654	0.492	2956	6.527	0.085	69	8.880	0.661	4952	8.183	0.078	270	8.428	0.328	4952	8.183	0.076	Suricata suricatta	Carnivora	Suricata_suricatta
Sus scrofa	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	30	8.267	1.021	269	8.448	0.385	Sus scrofa	Artiodactyla	Sus_scrofa
Symphalangus syndactylus	92	25.344	2.629	158	20.423	1.502	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Symphalangus syndactylus	Primates	Symphalangus_syndactylus
Trachypithecus cristatus	39	16.334	2.061	61	13.114	1.417	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Trachypithecus cristatus	Primates	Trachypithecus_cristatus
Trachypithecus francoisi	59	12.951	1.381	55	12.155	1.372	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Trachypithecus francoisi	Primates	Trachypithecus_francoisi
Tragelaphus oryx	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	159	9.504	0.661	1084	7.019	0.217	Tragelaphus oryx	Artiodactyla	Tragelaphus_oryx
Tragelaphus strepsiceros	35	6.728	0.743	649	6.715	0.190	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	43	5.565	0.658	476	4.753	0.184	Tragelaphus strepsiceros	Artiodactyla	Tragelaphus_strepsiceros
Ursus americanus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	71	19.686	1.339	235	18.107	0.722	Ursus americanus	Carnivora	Ursus_americanus
Ursus arctos	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	61	21.773	1.742	206	23.531	0.967	Ursus arctos	Carnivora	Ursus_arctos
Varecia rubra	160	17.404	1.025	397	16.249	0.569	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Varecia rubra	Primates	Varecia_rubra
Vicugna pacos	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	139	11.875	0.621	820	12.234	0.291	Vicugna pacos	Artiodactyla	Vicugna_pacos
Vulpes lagopus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	31	6.193	0.705	206	6.074	0.285	Vulpes lagopus	Carnivora	Vulpes_lagopus
Vulpes vulpes	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	64	12.126	1.113	466	9.264	0.296	Vulpes vulpes	Carnivora	Vulpes_vulpes
Vulpes zerda	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	40	6.240	0.519	478	7.368	0.178	Vulpes zerda	Carnivora	Vulpes_zerda
Zalophus californianus	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	36	10.913	1.145	296	12.483	0.506	Zalophus californianus	Carnivora	Zalophus_californianus

Processed data 1: contraceptive vs. normal

kable(dat_all, "html", escape = FALSE, digits = 3) %>% kable_styling("striped", position = "left", full_width = TRUE) %>% 
  scroll_box(width = "100%", 
    height = "500px")

species	order	phylogeny	sex	type	yi	vi	obs_id	sex_type
Aonyx cinerea	Carnivora	Aonyx_cinerea	male	hormonal	0.132	0.526	1	male_hormonal
Lycaon pictus	Carnivora	Lycaon_pictus	male	hormonal	-0.017	0.603	2	male_hormonal
Macaca silenus	Primates	Macaca_silenus	male	hormonal	0.058	0.405	3	male_hormonal
Mandrillus sphinx	Primates	Mandrillus_sphinx	male	hormonal	-0.035	0.422	4	male_hormonal
Procavia capensis	Hyracoidea	Procavia_capensis	male	hormonal	0.129	1.334	5	male_hormonal
Saguinus oedipus	Primates	Saguinus_oedipus	male	hormonal	-0.023	0.521	6	male_hormonal
Suricata suricatta	Carnivora	Suricata_suricatta	male	hormonal	0.082	0.579	7	male_hormonal
Addax nasomaculatus	Artiodactyla	Addax_nasomaculatus	male	surgical	0.129	0.008	8	male_surgical
Aepyceros melampus	Artiodactyla	Aepyceros_melampus	male	surgical	0.498	0.010	9	male_surgical
Alouatta caraya	Primates	Alouatta_caraya	male	surgical	0.061	0.014	10	male_surgical
Ammotragus lervia	Artiodactyla	Ammotragus_lervia	male	surgical	-0.056	0.004	11	male_surgical
Antilope cervicapra	Artiodactyla	Antilope_cervicapra	male	surgical	0.499	0.004	12	male_surgical
Aonyx cinerea	Carnivora	Aonyx_cinerea	male	surgical	0.008	0.005	13	male_surgical
Ateles geoffroyi	Primates	Ateles_geoffroyi	male	surgical	-0.261	0.039	14	male_surgical
Bison bison	Artiodactyla	Bison_bison	male	surgical	0.141	0.008	15	male_surgical
Bos grunniens	Artiodactyla	Bos_grunniens	male	surgical	-0.199	0.028	16	male_surgical
Boselaphus tragocamelus	Artiodactyla	Boselaphus_tragocamelus	male	surgical	-0.098	0.032	17	male_surgical
Callithrix jacchus	Primates	Callithrix_jacchus	male	surgical	0.224	0.009	18	male_surgical
Camelus bactrianus	Artiodactyla	Camelus_bactrianus	male	surgical	-0.068	0.007	19	male_surgical
Camelus dromedarius	Artiodactyla	Camelus_dromedarius	male	surgical	-0.186	0.012	20	male_surgical
Canis latrans	Carnivora	Canis_latrans	male	surgical	0.128	0.005	21	male_surgical
Capra hircus	Artiodactyla	Capra_hircus	male	surgical	-0.028	0.001	22	male_surgical
Cavia porcellus	Rodentia	Cavia_porcellus	male	surgical	0.036	0.003	23	male_surgical
Cebus capucinus	Primates	Cebus_capucinus	male	surgical	0.327	0.016	24	male_surgical
Cervus elaphus	Artiodactyla	Cervus_elaphus	male	surgical	0.104	0.023	25	male_surgical
Chinchilla lanigera	Rodentia	Chinchilla_lanigera	male	surgical	0.138	0.027	26	male_surgical
Cynomys ludovicianus	Rodentia	Cynomys_ludovicianus	male	surgical	-0.081	0.009	27	male_surgical
Dama dama	Artiodactyla	Dama_dama	male	surgical	0.329	0.012	28	male_surgical
Desmodus rotundus	Chiroptera	Desmodus_rotundus	male	surgical	0.257	0.015	29	male_surgical
Didelphis virginiana	Didelphimorphia	Didelphis_virginiana	male	surgical	0.090	0.006	30	male_surgical
Dolichotis patagonum	Rodentia	Dolichotis_patagonum	male	surgical	0.092	0.010	31	male_surgical
Eidolon helvum	Chiroptera	Eidolon_helvum	male	surgical	0.114	0.003	32	male_surgical
Equus caballus	Perissodactyla	Equus_caballus	male	surgical	-0.063	0.008	33	male_surgical
Equus quagga	Perissodactyla	Equus_quagga	male	surgical	0.489	0.017	34	male_surgical
Erythrocebus patas	Primates	Erythrocebus_patas	male	surgical	-0.065	0.023	35	male_surgical
Eudorcas thomsonii	Artiodactyla	Eudorcas_thomsonii	male	surgical	0.437	0.014	36	male_surgical
Felis catus	Carnivora	Felis_catus	male	surgical	0.030	0.015	37	male_surgical
Hemitragus jemlahicus	Artiodactyla	Hemitragus_jemlahicus	male	surgical	-0.039	0.022	38	male_surgical
Hydrochoerus hydrochaeris	Rodentia	Hydrochoerus_hydrochaeris	male	surgical	-0.018	0.005	39	male_surgical
Kobus megaceros	Artiodactyla	Kobus_megaceros	male	surgical	0.141	0.014	40	male_surgical
Lama glama	Artiodactyla	Lama_glama	male	surgical	-0.077	0.004	41	male_surgical
Lama guanicoe	Artiodactyla	Lama_guanicoe	male	surgical	0.003	0.022	42	male_surgical
Lemur catta	Primates	Lemur_catta	male	surgical	0.097	0.002	43	male_surgical
Leptailurus serval	Carnivora	Leptailurus_serval	male	surgical	0.179	0.004	44	male_surgical
Lynx lynx	Carnivora	Lynx_lynx	male	surgical	0.081	0.012	45	male_surgical
Lynx rufus	Carnivora	Lynx_rufus	male	surgical	0.156	0.004	46	male_surgical
Macaca fascicularis	Primates	Macaca_fascicularis	male	surgical	0.147	0.038	47	male_surgical
Macaca fuscata	Primates	Macaca_fuscata	male	surgical	0.123	0.011	48	male_surgical
Macaca leonina	Primates	Macaca_leonina	male	surgical	-0.254	0.097	49	male_surgical
Macaca mulatta	Primates	Macaca_mulatta	male	surgical	0.293	0.010	50	male_surgical
Macaca nigra	Primates	Macaca_nigra	male	surgical	-0.192	0.020	51	male_surgical
Macaca silenus	Primates	Macaca_silenus	male	surgical	0.063	0.016	52	male_surgical
Macaca sylvanus	Primates	Macaca_sylvanus	male	surgical	0.371	0.008	53	male_surgical
Macropus fuliginosus	Diprotodontia	Macropus_fuliginosus	male	surgical	0.202	0.013	54	male_surgical
Macropus giganteus	Diprotodontia	Macropus_giganteus	male	surgical	0.189	0.008	55	male_surgical
Mandrillus sphinx	Primates	Mandrillus_sphinx	male	surgical	0.031	0.008	56	male_surgical
Mephitis mephitis	Carnivora	Mephitis_mephitis	male	surgical	-0.040	0.003	57	male_surgical
Mungos mungo	Carnivora	Mungos_mungo	male	surgical	-0.158	0.018	58	male_surgical
Muntiacus reevesi	Artiodactyla	Muntiacus_reevesi	male	surgical	0.235	0.013	59	male_surgical
Mustela nigripes	Carnivora	Mustela_nigripes	male	surgical	0.019	0.005	60	male_surgical
Myocastor coypus	Rodentia	Myocastor_coypus	male	surgical	-0.026	0.031	61	male_surgical
Nasua narica	Carnivora	Nasua_narica	male	surgical	-0.137	0.012	62	male_surgical
Nasua nasua	Carnivora	Nasua_nasua	male	surgical	0.132	0.002	63	male_surgical
Notamacropus parma	Diprotodontia	Macropus_parma	male	surgical	0.257	0.015	64	male_surgical
Notamacropus rufogriseus	Diprotodontia	Macropus_rufogriseus	male	surgical	0.163	0.002	65	male_surgical
Octodon degus	Rodentia	Octodon_degus	male	surgical	0.123	0.005	66	male_surgical
Odocoileus virginianus	Artiodactyla	Odocoileus_virginianus	male	surgical	0.371	0.025	67	male_surgical
Oryctolagus cuniculus	Lagomorpha	Oryctolagus_cuniculus	male	surgical	-0.018	0.009	68	male_surgical
Oryx dammah	Artiodactyla	Oryx_dammah	male	surgical	0.240	0.004	69	male_surgical
Osphranter rufus	Diprotodontia	Macropus_rufus	male	surgical	0.194	0.003	70	male_surgical
Ovis aries	Artiodactyla	Ovis_aries	male	surgical	-0.035	0.012	71	male_surgical
Pan troglodytes	Primates	Pan_troglodytes	male	surgical	0.299	0.006	72	male_surgical
Panthera leo	Carnivora	Panthera_leo	male	surgical	-0.028	0.002	73	male_surgical
Papio hamadryas	Primates	Papio_hamadryas	male	surgical	0.174	0.005	74	male_surgical
Papio papio	Primates	Papio_papio	male	surgical	-0.051	0.023	75	male_surgical
Pecari tajacu	Artiodactyla	Pecari_tajacu	male	surgical	0.091	0.008	76	male_surgical
Petaurus breviceps	Diprotodontia	Petaurus_breviceps	male	surgical	-0.059	0.007	77	male_surgical
Potamochoerus porcus	Artiodactyla	Potamochoerus_porcus	male	surgical	0.174	0.007	78	male_surgical
Procyon lotor	Carnivora	Procyon_lotor	male	surgical	0.244	0.002	79	male_surgical
Puma concolor	Carnivora	Puma_concolor	male	surgical	0.001	0.004	80	male_surgical
Rangifer tarandus	Artiodactyla	Rangifer_tarandus	male	surgical	0.329	0.016	81	male_surgical
Rattus norvegicus	Rodentia	Rattus_norvegicus	male	surgical	0.021	0.010	82	male_surgical
Rousettus aegyptiacus	Chiroptera	Rousettus_aegyptiacus	male	surgical	-0.204	0.008	83	male_surgical
Saguinus oedipus	Primates	Saguinus_oedipus	male	surgical	0.051	0.007	84	male_surgical
Saimiri sciureus	Primates	Saimiri_sciureus	male	surgical	0.434	0.009	85	male_surgical
Sapajus apella	Primates	Sapajus_apella	male	surgical	-0.119	0.012	86	male_surgical
Suricata suricatta	Carnivora	Suricata_suricatta	male	surgical	0.030	0.002	87	male_surgical
Sus scrofa	Artiodactyla	Sus_scrofa	male	surgical	-0.022	0.017	88	male_surgical
Tragelaphus oryx	Artiodactyla	Tragelaphus_oryx	male	surgical	0.303	0.006	89	male_surgical
Tragelaphus strepsiceros	Artiodactyla	Tragelaphus_strepsiceros	male	surgical	0.158	0.015	90	male_surgical
Ursus americanus	Carnivora	Ursus_americanus	male	surgical	0.084	0.006	91	male_surgical
Ursus arctos	Carnivora	Ursus_arctos	male	surgical	-0.078	0.008	92	male_surgical
Vicugna pacos	Artiodactyla	Vicugna_pacos	male	surgical	-0.030	0.003	93	male_surgical
Vulpes lagopus	Carnivora	Vulpes_lagopus	male	surgical	0.019	0.015	94	male_surgical
Vulpes vulpes	Carnivora	Vulpes_vulpes	male	surgical	0.269	0.009	95	male_surgical
Vulpes zerda	Carnivora	Vulpes_zerda	male	surgical	-0.166	0.008	96	male_surgical
Zalophus californianus	Carnivora	Zalophus_californianus	male	surgical	-0.134	0.013	97	male_surgical
Addax nasomaculatus	Artiodactyla	Addax_nasomaculatus	female	hormonal	0.082	0.009	98	female_hormonal
Alouatta caraya	Primates	Alouatta_caraya	female	hormonal	0.279	0.013	99	female_hormonal
Aonyx cinerea	Carnivora	Aonyx_cinerea	female	hormonal	0.156	0.004	100	female_hormonal
Axis axis	Artiodactyla	Axis_axis	female	hormonal	-0.517	0.036	101	female_hormonal
Callimico goeldii	Primates	Callimico_goeldii	female	hormonal	0.124	0.013	102	female_hormonal
Callithrix geoffroyi	Primates	Callithrix_geoffroyi	female	hormonal	-0.088	0.013	103	female_hormonal
Callithrix jacchus	Primates	Callithrix_jacchus	female	hormonal	0.060	0.009	104	female_hormonal
Cebuella pygmaea	Primates	Cebuella_pygmaea	female	hormonal	0.191	0.018	105	female_hormonal
Cercopithecus neglectus	Primates	Cercopithecus_neglectus	female	hormonal	0.012	0.010	106	female_hormonal
Colobus guereza	Primates	Colobus_guereza	female	hormonal	0.371	0.009	107	female_hormonal
Erythrocebus patas	Primates	Erythrocebus_patas	female	hormonal	0.153	0.011	108	female_hormonal
Eulemur macaco	Primates	Eulemur_macaco	female	hormonal	0.324	0.023	109	female_hormonal
Giraffa camelopardalis	Artiodactyla	Giraffa_camelopardalis	female	hormonal	0.378	0.011	110	female_hormonal
Hydrochoerus hydrochaeris	Rodentia	Hydrochoerus_hydrochaeris	female	hormonal	-0.065	0.011	111	female_hormonal
Lemur catta	Primates	Lemur_catta	female	hormonal	0.109	0.002	112	female_hormonal
Leontopithecus chrysomelas	Primates	Leontopithecus_chrysomelas	female	hormonal	0.212	0.007	113	female_hormonal
Leontopithecus rosalia	Primates	Leontopithecus_rosalia	female	hormonal	0.205	0.005	114	female_hormonal
Leptailurus serval	Carnivora	Leptailurus_serval	female	hormonal	-0.084	0.016	115	female_hormonal
Lycaon pictus	Carnivora	Lycaon_pictus	female	hormonal	0.061	0.006	116	female_hormonal
Macaca fuscata	Primates	Macaca_fuscata	female	hormonal	-0.055	0.005	117	female_hormonal
Macaca nemestrina	Primates	Macaca_nemestrina	female	hormonal	-0.080	0.032	118	female_hormonal
Macaca nigra	Primates	Macaca_nigra	female	hormonal	-0.015	0.012	119	female_hormonal
Macaca silenus	Primates	Macaca_silenus	female	hormonal	0.050	0.011	120	female_hormonal
Macaca sylvanus	Primates	Macaca_sylvanus	female	hormonal	0.157	0.005	121	female_hormonal
Mandrillus sphinx	Primates	Mandrillus_sphinx	female	hormonal	0.167	0.004	122	female_hormonal
Nasua nasua	Carnivora	Nasua_nasua	female	hormonal	-0.019	0.010	123	female_hormonal
Nomascus leucogenys	Primates	Nomascus_leucogenys	female	hormonal	0.003	0.019	124	female_hormonal
Octodon degus	Rodentia	Octodon_degus	female	hormonal	0.286	0.007	125	female_hormonal
Oryx dammah	Artiodactyla	Oryx_dammah	female	hormonal	0.053	0.010	126	female_hormonal
Pan troglodytes	Primates	Pan_troglodytes	female	hormonal	0.021	0.003	127	female_hormonal
Panthera leo	Carnivora	Panthera_leo	female	hormonal	0.120	0.001	128	female_hormonal
Panthera onca	Carnivora	Panthera_onca	female	hormonal	-0.010	0.005	129	female_hormonal
Papio hamadryas	Primates	Papio_hamadryas	female	hormonal	0.258	0.002	130	female_hormonal
Papio papio	Primates	Papio_papio	female	hormonal	0.000	0.014	131	female_hormonal
Phacochoerus africanus	Artiodactyla	Phacochoerus_africanus	female	hormonal	0.042	0.006	132	female_hormonal
Pithecia pithecia	Primates	Pithecia_pithecia	female	hormonal	0.204	0.017	133	female_hormonal
Pongo abelii	Primates	Pongo_abelii	female	hormonal	0.207	0.008	134	female_hormonal
Potamochoerus porcus	Artiodactyla	Potamochoerus_porcus	female	hormonal	-0.045	0.007	135	female_hormonal
Pteropus rodricensis	Chiroptera	Pteropus_rodricensis	female	hormonal	0.202	0.008	136	female_hormonal
Saguinus bicolor	Primates	Saguinus_bicolor	female	hormonal	0.260	0.022	137	female_hormonal
Saguinus oedipus	Primates	Saguinus_oedipus	female	hormonal	0.157	0.004	138	female_hormonal
Saimiri sciureus	Primates	Saimiri_sciureus	female	hormonal	0.203	0.010	139	female_hormonal
Suricata suricatta	Carnivora	Suricata_suricatta	female	hormonal	0.093	0.001	140	female_hormonal
Symphalangus syndactylus	Primates	Symphalangus_syndactylus	female	hormonal	0.216	0.016	141	female_hormonal
Trachypithecus cristatus	Primates	Trachypithecus_cristatus	female	hormonal	0.220	0.028	142	female_hormonal
Trachypithecus francoisi	Primates	Trachypithecus_francoisi	female	hormonal	0.063	0.024	143	female_hormonal
Tragelaphus strepsiceros	Artiodactyla	Tragelaphus_strepsiceros	female	hormonal	0.002	0.013	144	female_hormonal
Varecia rubra	Primates	Varecia_rubra	female	hormonal	0.069	0.005	145	female_hormonal
Lemur catta	Primates	Lemur_catta	female	surgical	-0.010	0.010	146	female_surgical
Leptailurus serval	Carnivora	Leptailurus_serval	female	surgical	0.152	0.011	147	female_surgical
Lycaon pictus	Carnivora	Lycaon_pictus	female	surgical	0.156	0.005	148	female_surgical
Nasua nasua	Carnivora	Nasua_nasua	female	surgical	-0.037	0.009	149	female_surgical
Pan troglodytes	Primates	Pan_troglodytes	female	surgical	0.130	0.008	150	female_surgical
Panthera leo	Carnivora	Panthera_leo	female	surgical	0.067	0.002	151	female_surgical
Papio hamadryas	Primates	Papio_hamadryas	female	surgical	0.233	0.004	152	female_surgical
Saguinus oedipus	Primates	Saguinus_oedipus	female	surgical	-0.102	0.022	153	female_surgical
Suricata suricatta	Carnivora	Suricata_suricatta	female	surgical	0.019	0.006	154	female_surgical

Processed data 2: sex differences

kable(dat_comb, "html", escape = FALSE, digits = 3) %>% kable_styling("striped", position = "left", full_width = TRUE) %>% 
  scroll_box(width = "100%", 
    height = "500px")

species	phylogeny	category	yi	vi	obs_id	contraception	abs_yi2	abs_yi	abs_vi
Cavia porcellus	Cavia_porcellus	M normal/F surgical	0.002	0.003	1	Male normal/ Female sterlized	0.002	0.045	0.001
Cynomys ludovicianus	Cynomys_ludovicianus	M normal/F surgical	-0.050	0.008	2	Male normal/ Female sterlized	0.050	0.084	0.004
Eidolon helvum	Eidolon_helvum	M normal/F surgical	-0.062	0.003	3	Male normal/ Female sterlized	0.062	0.069	0.002
Lemur catta	Lemur_catta	M normal/F surgical	0.141	0.002	4	Male normal/ Female sterlized	0.141	0.141	0.002
Leptailurus serval	Leptailurus_serval	M normal/F surgical	0.096	0.004	5	Male normal/ Female sterlized	0.096	0.100	0.004
Lynx rufus	Lynx_rufus	M normal/F surgical	0.294	0.004	6	Male normal/ Female sterlized	0.294	0.294	0.004
Mustela nigripes	Mustela_nigripes	M normal/F surgical	0.196	0.006	7	Male normal/ Female sterlized	0.196	0.196	0.006
Nasua nasua	Nasua_nasua	M normal/F surgical	-0.026	0.002	8	Male normal/ Female sterlized	0.026	0.041	0.001
Oryctolagus cuniculus	Oryctolagus_cuniculus	M normal/F surgical	0.091	0.008	9	Male normal/ Female sterlized	0.091	0.106	0.005
Pan troglodytes	Pan_troglodytes	M normal/F surgical	0.065	0.005	10	Male normal/ Female sterlized	0.065	0.079	0.003
Panthera leo	Panthera_leo	M normal/F surgical	0.112	0.002	11	Male normal/ Female sterlized	0.112	0.112	0.002
Papio hamadryas	Papio_hamadryas	M normal/F surgical	0.132	0.005	12	Male normal/ Female sterlized	0.132	0.133	0.004
Procyon lotor	Procyon_lotor	M normal/F surgical	0.087	0.002	13	Male normal/ Female sterlized	0.087	0.088	0.002
Rattus norvegicus	Rattus_norvegicus	M normal/F surgical	-0.013	0.010	14	Male normal/ Female sterlized	0.013	0.079	0.004
Saguinus oedipus	Saguinus_oedipus	M normal/F surgical	0.168	0.007	15	Male normal/ Female sterlized	0.168	0.169	0.007
Suricata suricatta	Suricata_suricatta	M normal/F surgical	0.256	0.002	16	Male normal/ Female sterlized	0.256	0.256	0.002
Aonyx cinerea	Aonyx_cinerea	M hormonal/F normal	-0.465	0.071	17	Male sterlized/ Female normal	0.465	0.473	0.063
Lycaon pictus	Lycaon_pictus	M hormonal/F normal	-0.374	0.058	18	Male sterlized/ Female normal	0.374	0.386	0.048
Macaca silenus	Macaca_silenus	M hormonal/F normal	-0.289	0.066	19	Male sterlized/ Female normal	0.289	0.322	0.045
Mandrillus sphinx	Mandrillus_sphinx	M hormonal/F normal	-0.368	0.063	20	Male sterlized/ Female normal	0.368	0.384	0.051
Saguinus oedipus	Saguinus_oedipus	M hormonal/F normal	-0.119	0.059	21	Male sterlized/ Female normal	0.119	0.216	0.026
Suricata suricatta	Suricata_suricatta	M hormonal/F normal	-0.022	0.056	22	Male sterlized/ Female normal	0.022	0.190	0.021
Cavia porcellus	Cavia_porcellus	M hormonal/F normal	0.190	0.008	23	Male sterlized/ Female normal	0.190	0.191	0.008
Cynomys ludovicianus	Cynomys_ludovicianus	M hormonal/F normal	0.014	0.020	24	Male sterlized/ Female normal	0.014	0.112	0.007
Eidolon helvum	Eidolon_helvum	M hormonal/F normal	-0.338	0.004	25	Male sterlized/ Female normal	0.338	0.338	0.004
Lemur catta	Lemur_catta	M hormonal/F normal	0.053	0.010	26	Male sterlized/ Female normal	0.053	0.091	0.005
Leptailurus serval	Leptailurus_serval	M hormonal/F normal	-0.236	0.011	27	Male sterlized/ Female normal	0.236	0.236	0.011
Lynx rufus	Lynx_rufus	M hormonal/F normal	0.150	0.006	28	Male sterlized/ Female normal	0.150	0.151	0.006
Mustela nigripes	Mustela_nigripes	M hormonal/F normal	0.153	0.007	29	Male sterlized/ Female normal	0.153	0.155	0.006
Nasua nasua	Nasua_nasua	M hormonal/F normal	-0.122	0.009	30	Male sterlized/ Female normal	0.122	0.131	0.007
Oryctolagus cuniculus	Oryctolagus_cuniculus	M hormonal/F normal	-0.111	0.014	31	Male sterlized/ Female normal	0.111	0.134	0.009
Pan troglodytes	Pan_troglodytes	M hormonal/F normal	-0.365	0.008	32	Male sterlized/ Female normal	0.365	0.365	0.008
Panthera leo	Panthera_leo	M hormonal/F normal	0.074	0.002	33	Male sterlized/ Female normal	0.074	0.075	0.001
Papio hamadryas	Papio_hamadryas	M hormonal/F normal	-0.276	0.004	34	Male sterlized/ Female normal	0.276	0.276	0.004
Procyon lotor	Procyon_lotor	M hormonal/F normal	-0.207	0.003	35	Male sterlized/ Female normal	0.207	0.207	0.003
Rattus norvegicus	Rattus_norvegicus	M hormonal/F normal	-0.171	0.018	36	Male sterlized/ Female normal	0.171	0.183	0.013
Saguinus oedipus	Saguinus_oedipus	M hormonal/F normal	0.219	0.022	37	Male sterlized/ Female normal	0.219	0.227	0.018
Suricata suricatta	Suricata_suricatta	M hormonal/F normal	0.207	0.006	38	Male sterlized/ Female normal	0.207	0.207	0.005
Cavia porcellus	Cavia_porcellus	M surgical/F surgical	0.225	0.011	39	Male sterlized/ Female sterlized	0.225	0.226	0.011
Cynomys ludovicianus	Cynomys_ludovicianus	M surgical/F surgical	-0.067	0.026	40	Male sterlized/ Female sterlized	0.067	0.140	0.011
Eidolon helvum	Eidolon_helvum	M surgical/F surgical	-0.224	0.005	41	Male sterlized/ Female sterlized	0.224	0.224	0.005
Lemur catta	Lemur_catta	M surgical/F surgical	0.151	0.012	42	Male sterlized/ Female sterlized	0.151	0.159	0.009
Leptailurus serval	Leptailurus_serval	M surgical/F surgical	-0.056	0.013	43	Male sterlized/ Female sterlized	0.056	0.103	0.006
Lynx rufus	Lynx_rufus	M surgical/F surgical	0.305	0.007	44	Male sterlized/ Female sterlized	0.305	0.305	0.007
Mustela nigripes	Mustela_nigripes	M surgical/F surgical	0.172	0.010	45	Male sterlized/ Female sterlized	0.172	0.175	0.009
Nasua nasua	Nasua_nasua	M surgical/F surgical	0.010	0.010	46	Male sterlized/ Female sterlized	0.010	0.080	0.004
Oryctolagus cuniculus	Oryctolagus_cuniculus	M surgical/F surgical	-0.129	0.019	47	Male sterlized/ Female sterlized	0.129	0.156	0.012
Pan troglodytes	Pan_troglodytes	M surgical/F surgical	-0.065	0.011	48	Male sterlized/ Female sterlized	0.065	0.099	0.005
Panthera leo	Panthera_leo	M surgical/F surgical	0.045	0.003	49	Male sterlized/ Female sterlized	0.045	0.056	0.002
Papio hamadryas	Papio_hamadryas	M surgical/F surgical	-0.101	0.007	50	Male sterlized/ Female sterlized	0.101	0.110	0.005
Procyon lotor	Procyon_lotor	M surgical/F surgical	0.037	0.004	51	Male sterlized/ Female sterlized	0.037	0.060	0.002
Rattus norvegicus	Rattus_norvegicus	M surgical/F surgical	-0.150	0.025	52	Male sterlized/ Female sterlized	0.150	0.179	0.016
Saguinus oedipus	Saguinus_oedipus	M surgical/F surgical	0.270	0.028	53	Male sterlized/ Female sterlized	0.270	0.277	0.024
Suricata suricatta	Suricata_suricatta	M surgical/F surgical	0.236	0.007	54	Male sterlized/ Female sterlized	0.236	0.236	0.007
Aonyx cinerea	Aonyx_cinerea	M normal/F normal	-0.309	0.001	55	Male normal/ Female normal	0.309	0.309	0.001
Lycaon pictus	Lycaon_pictus	M normal/F normal	-0.313	0.002	56	Male normal/ Female normal	0.313	0.313	0.002
Macaca silenus	Macaca_silenus	M normal/F normal	-0.240	0.005	57	Male normal/ Female normal	0.240	0.240	0.005
Mandrillus sphinx	Mandrillus_sphinx	M normal/F normal	-0.201	0.004	58	Male normal/ Female normal	0.201	0.201	0.004
Saguinus oedipus	Saguinus_oedipus	M normal/F normal	0.038	0.001	59	Male normal/ Female normal	0.038	0.042	0.001
Suricata suricatta	Suricata_suricatta	M normal/F normal	0.071	0.000	60	Male normal/ Female normal	0.071	0.071	0.000
Cavia porcellus	Cavia_porcellus	M normal/F normal	-0.034	0.000	61	Male normal/ Female normal	0.034	0.035	0.000
Cynomys ludovicianus	Cynomys_ludovicianus	M normal/F normal	0.031	0.002	62	Male normal/ Female normal	0.031	0.043	0.001
Eidolon helvum	Eidolon_helvum	M normal/F normal	-0.176	0.002	63	Male normal/ Female normal	0.176	0.176	0.002
Lemur catta	Lemur_catta	M normal/F normal	0.044	0.000	64	Male normal/ Female normal	0.044	0.044	0.000
Leptailurus serval	Leptailurus_serval	M normal/F normal	-0.084	0.002	65	Male normal/ Female normal	0.084	0.085	0.002
Lynx rufus	Lynx_rufus	M normal/F normal	0.139	0.003	66	Male normal/ Female normal	0.139	0.139	0.003
Mustela nigripes	Mustela_nigripes	M normal/F normal	0.177	0.003	67	Male normal/ Female normal	0.177	0.177	0.003
Nasua nasua	Nasua_nasua	M normal/F normal	-0.159	0.001	68	Male normal/ Female normal	0.159	0.159	0.001
Oryctolagus cuniculus	Oryctolagus_cuniculus	M normal/F normal	0.108	0.003	69	Male normal/ Female normal	0.108	0.110	0.003
Pan troglodytes	Pan_troglodytes	M normal/F normal	-0.234	0.003	70	Male normal/ Female normal	0.234	0.234	0.003
Panthera leo	Panthera_leo	M normal/F normal	0.140	0.001	71	Male normal/ Female normal	0.140	0.140	0.001
Papio hamadryas	Papio_hamadryas	M normal/F normal	-0.043	0.002	72	Male normal/ Female normal	0.043	0.049	0.001
Procyon lotor	Procyon_lotor	M normal/F normal	-0.157	0.001	73	Male normal/ Female normal	0.157	0.157	0.001
Rattus norvegicus	Rattus_norvegicus	M normal/F normal	-0.034	0.002	74	Male normal/ Female normal	0.034	0.046	0.001
Saguinus oedipus	Saguinus_oedipus	M normal/F normal	0.117	0.001	75	Male normal/ Female normal	0.117	0.117	0.001
Suricata suricatta	Suricata_suricatta	M normal/F normal	0.226	0.000	76	Male normal/ Female normal	0.226	0.226	0.000
Aonyx cinerea	Aonyx_cinerea	M normal/F hornomal	-0.177	0.010	77	NA	0.177	0.180	0.009
Lycaon pictus	Lycaon_pictus	M normal/F hornomal	-0.330	0.011	78	NA	0.330	0.330	0.011
Macaca silenus	Macaca_silenus	M normal/F hornomal	-0.182	0.025	79	NA	0.182	0.201	0.017
Mandrillus sphinx	Mandrillus_sphinx	M normal/F hornomal	-0.236	0.019	80	NA	0.236	0.241	0.017
Saguinus oedipus	Saguinus_oedipus	M normal/F hornomal	0.015	0.018	81	NA	0.015	0.107	0.007
Suricata suricatta	Suricata_suricatta	M normal/F hornomal	0.153	0.006	82	NA	0.153	0.154	0.005
Aonyx cinerea	Aonyx_cinerea	M hornomal/F hornomal	-0.332	0.013	83	Male sterlized/ Female sterlized	0.332	0.333	0.013
Lycaon pictus	Lycaon_pictus	M hornomal/F hornomal	-0.391	0.016	84	Male sterlized/ Female sterlized	0.391	0.391	0.015
Macaca silenus	Macaca_silenus	M hornomal/F hornomal	-0.231	0.032	85	Male sterlized/ Female sterlized	0.231	0.248	0.024
Mandrillus sphinx	Mandrillus_sphinx	M hornomal/F hornomal	-0.403	0.020	86	Male sterlized/ Female sterlized	0.403	0.403	0.020
Saguinus oedipus	Saguinus_oedipus	M hornomal/F hornomal	-0.142	0.021	87	Male sterlized/ Female sterlized	0.142	0.166	0.013
Suricata suricatta	Suricata_suricatta	M hornomal/F hornomal	0.059	0.007	88	Male sterlized/ Female sterlized	0.059	0.083	0.004

21 Analysis set 1: contraceptive vs. normal

Main meta-analysis

# variance-covariance matrix for sampling error assuming 0.5 correlation
VCV <- vcalc(vi = dat_all$vi, 
             cluster = dat_all$species, 
             obs = dat_all$obs_id,
             rho = 0.5, 
             data = dat_all)


mod_all <- rma.mv(yi, V = VCV,
                     random = list(
                       ~1|species,
                       ~1|phylogeny,
                       ~1|obs_id),
                     R = list(phylogeny = cor_tree),
                     data = dat_all,
                     test = "t",
                     sparse = TRUE)
summary(mod_all)

Multivariate Meta-Analysis Model (k = 154; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  63.4653  -126.9306  -118.9306  -106.8089  -118.6603   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0007  0.0272    114     no    species   no 
sigma^2.2  0.0047  0.0688    114     no  phylogeny  yes 
sigma^2.3  0.0097  0.0987    154     no     obs_id   no 

Test for Heterogeneity:
Q(df = 153) = 451.1870, p-val < .0001

Model Results:

estimate      se    tval   df    pval   ci.lb   ci.ub    
  0.0994  0.0445  2.2340  153  0.0269  0.0115  0.1872  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_all), 2)

    I2_Total   I2_species I2_phylogeny    I2_obs_id 
       70.07         3.42        21.76        44.89 

#robust(mod_all, cluster = species)  

orchard_plot(mod_all, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regression: sex

mod_all1 <- rma.mv(yi, V = VCV,
                   mod = ~ sex,
                  random = list(
                    ~1|species,
                    ~1|phylogeny,
                    ~1|obs_id),
                  R = list(phylogeny = cor_tree),
                  data = dat_all,
                  test = "t",
                  sparse = TRUE)
summary(mod_all1)

Multivariate Meta-Analysis Model (k = 154; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  62.4534  -124.9069  -114.9069   -99.7875  -114.4959   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0004  0.0212    114     no    species   no 
sigma^2.2  0.0048  0.0691    114     no  phylogeny  yes 
sigma^2.3  0.0101  0.1006    154     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 152) = 448.6429, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 152) = 0.0004, p-val = 0.9844

Model Results:

         estimate      se    tval   df    pval    ci.lb   ci.ub    
intrcpt    0.0990  0.0483  2.0484  152  0.0422   0.0035  0.1944  * 
sexmale    0.0005  0.0243  0.0196  152  0.9844  -0.0475  0.0485    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_all1b <- rma.mv(yi, V = VCV,
                   mod = ~ sex - 1,
                   random = list(
                     ~1|species,
                     ~1|phylogeny,
                     ~1|obs_id),
                   R = list(phylogeny = cor_tree),
                   data = dat_all,
                   test = "t",
                   sparse = TRUE)
summary(mod_all1b)

Multivariate Meta-Analysis Model (k = 154; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  62.4534  -124.9069  -114.9069   -99.7875  -114.4959   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0004  0.0212    114     no    species   no 
sigma^2.2  0.0048  0.0691    114     no  phylogeny  yes 
sigma^2.3  0.0101  0.1006    154     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 152) = 448.6429, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 152) = 2.4724, p-val = 0.0878

Model Results:

           estimate      se    tval   df    pval   ci.lb   ci.ub    
sexfemale    0.0990  0.0483  2.0484  152  0.0422  0.0035  0.1944  * 
sexmale      0.0994  0.0451  2.2071  152  0.0288  0.0104  0.1885  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_all1), 5)

   R2_marginal R2_conditional 
       0.00000        0.34046 

orchard_plot(mod_all1, mod = "sex",
             xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regression: type

dat_all$type <- factor(dat_all$type, 
                   levels = rev(c("surgical", "hormonal")))

mod_all2 <- rma.mv(yi, V = VCV,
                 mod = ~ type -1,
                 random = list(
                   ~1|species,
                   ~1|phylogeny,
                   ~1|obs_id),
                 R = list(phylogeny = cor_tree),
                 data = dat_all,
                  test = "t",
                  sparse = TRUE)

summary(mod_all2)

Multivariate Meta-Analysis Model (k = 154; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  62.4737  -124.9473  -114.9473   -99.8279  -114.5364   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0003  0.0165    114     no    species   no 
sigma^2.2  0.0047  0.0684    114     no  phylogeny  yes 
sigma^2.3  0.0103  0.1014    154     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 152) = 447.7283, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 152) = 2.5200, p-val = 0.0838

Model Results:

              estimate      se    tval   df    pval   ci.lb   ci.ub    
typehormonal    0.1012  0.0485  2.0850  152  0.0387  0.0053  0.1970  * 
typesurgical    0.0989  0.0446  2.2175  152  0.0281  0.0108  0.1870  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# R^2 - hardly any
round(r2_ml(mod_all2)*100, 5)

   R2_marginal R2_conditional 
       0.00759       32.51422 

orchard_plot(mod_all2, mod = "type",
             xlab = "lnRR (all)", group = "species", g = FALSE, angle = 90)

# result table
all_models(mod_all2, mod = "type")

Fixed effect	Estimate	Lower CI [0.025]	Upper CI [0.975]	P value	Lower PI [0.025]	Upper PI [0.975]
Hormonal	0.101	0.005	0.197	0.039	-0.161	0.363
Surgical	0.099	0.011	0.187	0.028	-0.160	0.358
Hormonal-Surgical	-0.002	-0.051	0.047	0.929	-0.249	0.245

Meta-regression: sex_type

# creating a new variable combining sex and type
dat_all$sex_type <- as.factor(paste(dat_all$sex, dat_all$type, sep = "_"))

mod_all3 <- rma.mv(yi, V = VCV,
                 mod = ~ sex_type -1,
                 random = list(
                   ~1|species,
                   ~1|phylogeny,
                   ~1|obs_id),
                 R = list(phylogeny = cor_tree),
                 data = dat_all,
                  test = "t",
                  sparse = TRUE)

summary(mod_all3)

Multivariate Meta-Analysis Model (k = 154; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  61.8583  -123.7165  -109.7165   -88.6421  -108.9278   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0000  0.0001    114     no    species   no 
sigma^2.2  0.0046  0.0680    114     no  phylogeny  yes 
sigma^2.3  0.0107  0.1035    154     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 150) = 447.6126, p-val < .0001

Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 150) = 1.2790, p-val = 0.2808

Model Results:

                         estimate      se    tval   df    pval    ci.lb   ci.ub 
sex_typefemale_hormonal    0.1010  0.0485  2.0814  150  0.0391   0.0051  0.1968 
sex_typefemale_surgical    0.0937  0.0621  1.5079  150  0.1337  -0.0291  0.2165 
sex_typemale_hormonal      0.1224  0.2654  0.4611  150  0.6454  -0.4021  0.6468 
sex_typemale_surgical      0.0993  0.0445  2.2311  150  0.0272   0.0114  0.1872 
                           
sex_typefemale_hormonal  * 
sex_typefemale_surgical    
sex_typemale_hormonal      
sex_typemale_surgical    * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# R^2 - hardly any
round(r2_ml(mod_all3)*100, 5)

   R2_marginal R2_conditional 
       0.16621       30.26889 

orchard_plot(mod_all3, mod = "sex_type",
             xlab = "lnRR (all)", group = "species", g = FALSE, angle = 90)

# result table
all_models(mod_all3, mod = "sex_type")

Fixed effect	Estimate	Lower CI [0.025]	Upper CI [0.975]	P value	Lower PI [0.025]	Upper PI [0.975]
Female_hormonal	0.101	0.005	0.197	0.039	-0.162	0.364
Female_surgical	0.094	-0.029	0.216	0.134	-0.180	0.367
Male_hormonal	0.122	-0.402	0.647	0.645	-0.456	0.701
Male_surgical	0.099	0.011	0.187	0.027	-0.161	0.359
Female_hormonal-Female_surgical	-0.007	-0.100	0.086	0.878	-0.267	0.253
Female_hormonal-Male_hormonal	0.021	-0.491	0.534	0.935	-0.546	0.588
Female_hormonal-Male_surgical	-0.002	-0.053	0.049	0.949	-0.250	0.246
Female_surgical-Male_hormonal	0.029	-0.484	0.541	0.913	-0.539	0.596
Female_surgical-Male_surgical	0.006	-0.085	0.096	0.904	-0.254	0.265
Male_hormonal-Male_surgical	-0.023	-0.536	0.490	0.930	-0.591	0.544

22 Analysis set 2: sex difference

Different types of sex differences

# variance-covariance matrix for sampling error assuming 0.5 correlation
VCV <- vcalc(vi = dat_comb$vi, 
             cluster = dat_comb$species, 
             obs = dat_comb$obs_id,
             rho = 0.5, 
             data = dat_comb)

mod_comb <- rma.mv(yi, V = VCV,
                   mods = ~contraception - 1,
                   random = list(
                     ~1|species,
                     ~1|phylogeny,
                     ~1|obs_id),
                   R = list(phylogeny = cor_tree),
                   data = dat_comb,
                   control = list(optimizer = "Nelder-Mead"),
                    test = "t",
                  sparse = TRUE)
summary(mod_comb)

Multivariate Meta-Analysis Model (k = 82; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 47.0117  -94.0234  -80.0234  -63.5264  -78.4234   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0183  0.1351     20     no    species   no 
sigma^2.2  0.0041  0.0642     20     no  phylogeny  yes 
sigma^2.3  0.0051  0.0714     82     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 78) = 780.6119, p-val < .0001

Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 78) = 6.6866, p-val = 0.0001

Model Results:

                                                estimate      se     tval  df 
contraceptionMale sterlized/\nFemale sterlized   -0.0367  0.0644  -0.5701  78 
contraceptionMale sterlized/\nFemale normal      -0.1080  0.0646  -1.6708  78 
contraceptionMale normal/\nFemale sterlized       0.0492  0.0631   0.7799  78 
contraceptionMale normal/\nFemale normal         -0.0518  0.0608  -0.8526  78 
                                                  pval    ci.lb   ci.ub    
contraceptionMale sterlized/\nFemale sterlized  0.5703  -0.1649  0.0915    
contraceptionMale sterlized/\nFemale normal     0.0988  -0.2366  0.0207  . 
contraceptionMale normal/\nFemale sterlized     0.4378  -0.0764  0.1747    
contraceptionMale normal/\nFemale normal        0.3965  -0.1728  0.0692    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_comb)*100, 5)

   R2_marginal R2_conditional 
       9.41548       83.17844 

#robust(mod_comb, cluster = species)  

orchard_plot(mod_comb, mod = "contraception",
             xlab = "log response ratio (lnRR)", group = "species", 
             g = FALSE, angle = 45)

all_models(mod_comb, mod = "contraception")  

Fixed effect	Estimate	Lower CI [0.025]	Upper CI [0.975]	P value	Lower PI [0.025]	Upper PI [0.975]
Male sterlized/ Female sterlized	-0.037	-0.165	0.091	0.570	-0.391	0.317
Male sterlized/ Female normal	-0.108	-0.237	0.021	0.099	-0.462	0.246
Male normal/ Female sterlized	0.049	-0.076	0.175	0.438	-0.304	0.402
Male normal/ Female normal	-0.052	-0.173	0.069	0.397	-0.403	0.300
Male sterlized/ Female sterlized-Male sterlized/ Female normal	-0.071	-0.134	-0.009	0.026	-0.402	0.260
Male sterlized/ Female sterlized-Male normal/ Female sterlized	0.086	0.024	0.148	0.007	-0.245	0.417
Male sterlized/ Female sterlized-Male normal/ Female normal	-0.015	-0.073	0.043	0.612	-0.345	0.315
Male sterlized/ Female normal-Male normal/ Female sterlized	0.157	0.095	0.219	0.000	-0.174	0.488
Male sterlized/ Female normal-Male normal/ Female normal	0.056	-0.003	0.116	0.065	-0.274	0.387
Male normal/ Female sterlized-Male normal/ Female normal	-0.101	-0.156	-0.046	0.000	-0.430	0.229

# adding only model figs
# plotting just male analyses - adjusting mod_comp_sex

res_sex <- mod_results(mod_comb, mod = "contraception", group = "species")

attr(res_sex, "class") <- NULL

res_sex$mod_table <- res_sex$mod_table[c(2, 4), ]
res_sex$data <- res_sex$data %>% filter(moderator == "Male sterlized/\nFemale normal"| moderator ==  "Male normal/\nFemale normal") 

res_sex2 <- res_sex

class(res_sex2) <-  c("orchard", "data.frame")

d01 <- orchard_plot(res_sex2, mod = "contraception", xlab = "log response ratio (lnRR)", 
                   group = "species", 
                   cb = T, flip = FALSE) 

d01

Different types of sex differences: absolute valies

# variance-covariance matrix for sampling error assuming 0.5 correlation

VCVa <- vcalc(vi = dat_comb$abs_vi, 
             cluster = dat_comb$species, 
             obs = dat_comb$obs_id,
             rho = 0.5, 
             data = dat_comb)



mod_comb_a <- rma.mv(abs_yi, V = VCVa,
                     mods = ~contraception - 1,
                     random = list(
                       ~1|species,
                       ~1|phylogeny,
                       ~1|obs_id),
                     R = list(phylogeny = cor_tree),
                     data = dat_comb,
                     control = list(optimizer = "Nelder-Mead"),
                      test = "t",
                     sparse = TRUE)
summary(mod_comb_a)

Multivariate Meta-Analysis Model (k = 82; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  78.3730  -156.7461  -142.7461  -126.2491  -141.1461   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0009  0.0296     20     no    species   no 
sigma^2.2  0.0015  0.0390     20     no  phylogeny  yes 
sigma^2.3  0.0035  0.0591     82     no     obs_id   no 

Test for Residual Heterogeneity:
QE(df = 78) = 384.1271, p-val < .0001

Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 78) = 5.7942, p-val = 0.0004

Model Results:

                                                estimate      se    tval  df 
contraceptionMale sterlized/\nFemale sterlized    0.1451  0.0383  3.7864  78 
contraceptionMale sterlized/\nFemale normal       0.1852  0.0389  4.7650  78 
contraceptionMale normal/\nFemale sterlized       0.1199  0.0366  3.2741  78 
contraceptionMale normal/\nFemale normal          0.1240  0.0342  3.6227  78 
                                                  pval   ci.lb   ci.ub      
contraceptionMale sterlized/\nFemale sterlized  0.0003  0.0688  0.2214  *** 
contraceptionMale sterlized/\nFemale normal     <.0001  0.1078  0.2626  *** 
contraceptionMale normal/\nFemale sterlized     0.0016  0.0470  0.1928   ** 
contraceptionMale normal/\nFemale normal        0.0005  0.0559  0.1922  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_comb_a)*100, 5)

   R2_marginal R2_conditional 
      10.38107       46.81997 

#robust(mod_comb_a, cluster = species)  

orchard_plot(mod_comb_a, mod = "contraception",
             xlab = "absolute log response ratio (lnRR)", group = "species", 
             g = FALSE, angle = 45)

all_models(mod_comb_a, mod = "contraception", type = "abs")

Fixed effect	Estimate	Lower CI [0.025]	Upper CI [0.975]	P value	Lower PI [0.025]	Upper PI [0.975]
Male sterlized/ Female sterlized	0.145	0.069	0.221	0.000	-0.026	0.316
Male sterlized/ Female normal	0.185	0.108	0.263	0.000	0.014	0.357
Male normal/ Female sterlized	0.120	0.047	0.193	0.002	-0.049	0.289
Male normal/ Female normal	0.124	0.056	0.192	0.001	-0.043	0.291
Male sterlized/ Female sterlized-Male sterlized/ Female normal	0.040	-0.012	0.093	0.134	-0.119	0.199
Male sterlized/ Female sterlized-Male normal/ Female sterlized	-0.025	-0.076	0.026	0.335	-0.184	0.134
Male sterlized/ Female sterlized-Male normal/ Female normal	-0.021	-0.069	0.027	0.389	-0.179	0.137
Male sterlized/ Female normal-Male normal/ Female sterlized	-0.065	-0.118	-0.013	0.015	-0.225	0.094
Male sterlized/ Female normal-Male normal/ Female normal	-0.061	-0.111	-0.011	0.016	-0.220	0.097
Male normal/ Female sterlized-Male normal/ Female normal	0.004	-0.041	0.049	0.858	-0.153	0.161

# plotting just male analyses - adjusting mod_comp_sex

res_sex_ab <- mod_results(mod_comb_a, mod = "contraception", group = "species")

attr(res_sex_ab, "class") <- NULL

res_sex_ab$mod_table <- res_sex_ab$mod_table[c(2, 4), ]
res_sex_ab$data <- res_sex_ab$data %>% filter(moderator == "Male sterlized/\nFemale normal"| moderator ==  "Male normal/\nFemale normal") 

res_sex_ab2 <- res_sex_ab

class(res_sex_ab2) <-  c("orchard", "data.frame")

d11 <- orchard_plot(res_sex_ab2, mod = "contraception", xlab = "log response ratio (lnRR)", 
                   group = "species", 
                   cb = T, flip = FALSE) 

d11

d01 / d11

# saving results for figs rds

saveRDS(res_sex2, file = here("Rdata", "fig", "res_sex_zoo.rds"))
saveRDS(res_sex_abs2, file = here("Rdata", "fig", "res_sex_zoo_abs.rds"))

23 Pre-post puberty analysis

This analysis compares the effect size of the lifespan extension before and after puberty.

23.1 Loading data and obtaining effect isze

#dat0 <- read_csv(here("data", "zoo", "timing_male.csv"), na = c("", "NA"))
dat0 <- read_csv(here("data", "zoo", "resultsBaSTAbefAftMatur.csv"), na = c("", "NA"))


# phylogeny
# read RData
#dford 

load(here("Rdata", "zoo", "maxCredTreeMammals.RData"))

tree <- maxCred #read.tree(here("data", "zoo", "tree_zoo.tre"))

# taxonomy
tax <- read.csv(here("data", "zoo", "species_merge_list.csv"))

dat0 %>% left_join(tax, by = c("Species" = "ZIMSSpecies")) -> dat_full

# talking out species with no data (Pseudocheirus peregrinus = likely to be mistaks in data)
# dat_full %>% filter(Species != "Chrysocyon brachyurus" &
#                     Species != "Crocuta crocuta" &
#                     Species != "Neofelis nebulosa" &
#                     Species != "Panthera uncia" &
#                     Species != "Pseudocheirus peregrinus") %>% 
#   mutate(phylogeny = gsub(" ", "_", vertlife.species)) -> dat

dat_full %>% 
  mutate(phylogeny = gsub(" ", "_", vertlife.treename)) -> dat1

# adding Cervus canadensis
# dat$vertlife.species[which(dat$Species == "Cervus canadensis")] <-"Cervus canadensis"
# dat$phylogeny[which(dat$Species == "Cervus canadensis")] <-"Cervus_canadensis"

# fixing species names
#dat$Species[dat$Species == "Equus asinus"] <- "Equus_africanus"
dat1$Species[dat1$Species == "Aonyx cinereus"] <- "Aonyx cinerea"
#dat$Species[dat$Species == "Bubalus bubalis"] <- "Bubalus arnee"


# life span data 
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% unique(dat1$phylogeny)))]

tree <- drop.tip(tree, to_drop)

# checking the number of spp
#length(tree$tip.label)

tree <- as.ultrametric(tree)

#tree <- compute.brlen(tree)
cor_tree <- vcv(tree, corr = TRUE)


missing <- which(is.na(dat$phylogeny))

dat$Species[missing]

NULL

# creating effect size 

dat <- escalc("ROM", 
                    m2i = LifeExpContraAft,
                    m1i = LifeExpContraBef,
                    sd2i = SEcontraAft*sqrt(NContraAft),
                    sd1i = SEcontraBef*sqrt(NContraBef), 
                    n2i = NContraAft,
                    n1i = NContraBef,
                    data = dat1
)

dat$species <- factor(dat$Species)

# another dataset where we have lnRR for both pre and post puberty

dat2 <- escalc("ROM", 
                    m1i = LifeExpContraBef,
                    m2i = LifeExpNoContra,
                    sd1i = SEcontraBef*sqrt(NContraBef),
                    sd2i = SEnoContra*sqrt(NnoContra),
                    n1i = NContraBef,
                    n2i = NnoContra,
                    data = dat1
)


dat3 <- escalc("ROM", 
                    m1i = LifeExpContraAft,
                    m2i = LifeExpNoContra,
                    sd1i = SEcontraAft*sqrt(NContraAft),
                    sd2i = SEnoContra*sqrt(NnoContra),
                    n1i = NContraAft,
                    n2i = NnoContra,
                    data = dat1
)

dat_long <- rbind(dat2, dat3)

dat_long$pairID <- rep(1:nrow(dat), 2)

dat_long$species <- factor(dat_long$Species)

# pre-post
dat_long$pre_post <- c(rep("pre", nrow(dat)), rep("post", nrow(dat)))

23.2 Meta-analysis and meta-regression

Meta-analysis: main

# meta-analysis

# variance-covariance matrix for sampling error assuming 0.5 correlation

mod_pp <- rma.mv(yi, V = vi,
                  random = list(
                    ~1|species,
                    ~1|phylogeny),
                  R = list(phylogeny = cor_tree),
                  data = dat,
                  test = "t",
                  sparse = TRUE)
summary(mod_pp)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.1930  -40.3860  -34.3860  -28.9660  -33.8006   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0006  0.0252     46     no    species   no 
sigma^2.2  0.0107  0.1033     46     no  phylogeny  yes 

Test for Heterogeneity:
Q(df = 45) = 56.2752, p-val = 0.1209

Model Results:

estimate      se    tval  df    pval    ci.lb   ci.ub    
  0.0648  0.0662  0.9789  45  0.3329  -0.0685  0.1982    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_pp), 5)

    I2_Total   I2_species I2_phylogeny 
    41.54681      2.33760     39.20922 

#robust(mod_all, cluster = species)  

orchard_plot(mod_pp, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-analysis without primates

# data set excluding primates

dat %>% filter(Primate == "No") -> dat_short


mod_pp2 <- rma.mv(yi, V = vi,
                 random = list(
                   ~1|species,
                   ~1|phylogeny),
                 R = list(phylogeny = cor_tree),
                 data = dat_short,
                  test = "t",
                  sparse = TRUE)
summary(mod_pp2)

Multivariate Meta-Analysis Model (k = 36; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 18.3479  -36.6958  -30.6958  -26.0297  -29.9216   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0021  0.0460     36     no    species   no 
sigma^2.2  0.0018  0.0420     36     no  phylogeny  yes 

Test for Heterogeneity:
Q(df = 35) = 38.5141, p-val = 0.3135

Model Results:

estimate      se    tval  df    pval   ci.lb   ci.ub    
  0.0830  0.0360  2.3028  35  0.0274  0.0098  0.1561  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_pp2), 5)

    I2_Total   I2_species I2_phylogeny 
    20.91951     11.41737      9.50214 

#robust(mod_all, cluster = species)  

orchard_plot(mod_pp2, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regression (Primate)

# primate as moderator

mod_pp3 <- rma.mv(yi, V = vi,
                  mod = ~ Primate,
                  random = list(
                    ~1|species,
                    ~1|phylogeny),
                  R = list(phylogeny = cor_tree),
                  data = dat,
                   test = "t",
                  sparse = TRUE)
summary(mod_pp3)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.8267  -41.6535  -33.6535  -26.5167  -32.6279   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0024  0.0488     46     no    species   no 
sigma^2.2  0.0026  0.0511     46     no  phylogeny  yes 

Test for Residual Heterogeneity:
QE(df = 44) = 50.4050, p-val = 0.2350

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 44) = 4.0940, p-val = 0.0491

Model Results:

            estimate      se     tval  df    pval    ci.lb    ci.ub    
intrcpt       0.0843  0.0408   2.0689  44  0.0445   0.0022   0.1665  * 
PrimateYes   -0.1316  0.0651  -2.0234  44  0.0491  -0.2628  -0.0005  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_pp3), 5)

    R2_marginal R2_conditional1 R2_conditional2 
        0.37649         0.70212         0.67436 

mod_pp3b <- rma.mv(yi, V = vi,
                  mod = ~ Primate - 1,
                  random = list(
                    ~1|species,
                    ~1|phylogeny),
                  R = list(phylogeny = cor_tree),
                  data = dat,
                   test = "t",
                  sparse = TRUE)
summary(mod_pp3b)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.8267  -41.6535  -33.6535  -26.5167  -32.6279   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0024  0.0488     46     no    species   no 
sigma^2.2  0.0026  0.0511     46     no  phylogeny  yes 

Test for Residual Heterogeneity:
QE(df = 44) = 50.4050, p-val = 0.2350

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 44) = 3.2731, p-val = 0.0473

Model Results:

            estimate      se     tval  df    pval    ci.lb   ci.ub    
PrimateNo     0.0843  0.0408   2.0689  44  0.0445   0.0022  0.1665  * 
PrimateYes   -0.0473  0.0664  -0.7124  44  0.4800  -0.1812  0.0866    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

orchard_plot(mod_pp3, mod = "Primate", xlab = "lnRR (all)", group = "species", g = FALSE)

#robust(mod_all, cluster = species)  

orchard_plot(mod_pp2, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-analysis without phylogeny

# no phylogeny

mod_np <- rma.mv(yi, V = vi,
                 random = list(
                   ~1|species),
                 data = dat,
                  test = "t",
                  sparse = TRUE)
summary(mod_np)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 18.5945  -37.1891  -33.1891  -29.5757  -32.9034   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2    0.0027  0.0522     46     no  species 

Test for Heterogeneity:
Q(df = 45) = 56.2752, p-val = 0.1209

Model Results:

estimate      se    tval  df    pval   ci.lb   ci.ub     
  0.0562  0.0207  2.7146  45  0.0094  0.0145  0.0980  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_np), 5)

  I2_Total I2_species 
  14.61126   14.61126 

#robust(mod_all, cluster = species)  

orchard_plot(mod_np, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regression (Primate) witout phylogeny

# phlo - primates as a separate group 
mod_np2 <- rma.mv(yi, V = vi,
                 mod = ~ Primate,
                 random = list(
                   ~1|species),
                 data = dat,
                  test = "t",
                  sparse = TRUE)
summary(mod_np2)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.7305  -41.4610  -35.4610  -30.1084  -34.8610   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2    0.0032  0.0563     46     no  species 

Test for Residual Heterogeneity:
QE(df = 44) = 50.4050, p-val = 0.2350

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 44) = 6.1333, p-val = 0.0172

Model Results:

            estimate      se     tval  df    pval    ci.lb    ci.ub     
intrcpt       0.0795  0.0230   3.4506  44  0.0012   0.0330   0.1259  ** 
PrimateYes   -0.1404  0.0567  -2.4765  44  0.0172  -0.2547  -0.0261   * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_np2), 5)

   R2_marginal R2_conditional 
       0.51948        0.51948 

mod_np2b <- rma.mv(yi, V = vi,
                 mod = ~ Primate -1,
                 random = list(
                   ~1|species),
                 data = dat,
                  test = "t",
                  sparse = TRUE)
summary(mod_np2b)

Multivariate Meta-Analysis Model (k = 46; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 20.7305  -41.4610  -35.4610  -30.1084  -34.8610   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2    0.0032  0.0563     46     no  species 

Test for Residual Heterogeneity:
QE(df = 44) = 50.4050, p-val = 0.2350

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 44) = 6.6456, p-val = 0.0030

Model Results:

            estimate      se     tval  df    pval    ci.lb   ci.ub     
PrimateNo     0.0795  0.0230   3.4506  44  0.0012   0.0330  0.1259  ** 
PrimateYes   -0.0610  0.0518  -1.1767  44  0.2456  -0.1654  0.0435     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#robust(mod_all, cluster = species)  

orchard_plot(mod_np2, mod = "Primate", xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-analysis: alternative (with phylogeny)

# meta-analysis

# variance-covariance matrix for sampling error assuming 0.5 correlation

mod_pp2 <- rma.mv(yi, V = vi,
                  random = list(
                    ~1|species,
                    ~1|phylogeny,
                    ~1|pairID),
                  R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_pp2)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 33.9039  -67.8079  -59.8079  -49.7645  -59.3428   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0077  0.0877     46     no    species   no 
sigma^2.2  0.0104  0.1021     46     no  phylogeny  yes 
sigma^2.3  0.0077  0.0877     46     no     pairID   no 

Test for Heterogeneity:
Q(df = 91) = 303.2831, p-val < .0001

Model Results:

estimate      se    tval  df    pval    ci.lb   ci.ub    
  0.1157  0.0672  1.7217  91  0.0885  -0.0178  0.2491  . 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_pp2), 5)

    I2_Total   I2_species I2_phylogeny    I2_pairID 
    76.42277     22.77885     30.86508     22.77885 

#robust(mod_all, cluster = species)  

orchard_plot(mod_pp2, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regressoin: pre vs. post (with phylogeny)

mod_pp3 <- rma.mv(yi, V = vi,
                  mod = ~ pre_post - 1,
                  random = list(
                    ~1|species,
                    ~1|phylogeny,
                    ~1|pairID),
                  R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_pp3)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 36.3442  -72.6885  -62.6885  -50.1894  -61.9742   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0080  0.0892     46     no    species   no 
sigma^2.2  0.0099  0.0996     46     no  phylogeny  yes 
sigma^2.3  0.0080  0.0892     46     no     pairID   no 

Test for Residual Heterogeneity:
QE(df = 90) = 300.4417, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 90) = 5.4475, p-val = 0.0058

Model Results:

              estimate      se    tval  df    pval    ci.lb   ci.ub    
pre_postpost    0.0892  0.0667  1.3388  90  0.1840  -0.0432  0.2217    
pre_postpre     0.1432  0.0667  2.1469  90  0.0345   0.0107  0.2757  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_pp3), 5)

    R2_marginal R2_conditional1 R2_conditional2 R2_conditional3 
        0.02769         0.70051         0.62667         0.70051 

mod_pp3b <- rma.mv(yi, V = vi,
                  mod = ~ pre_post,
                  random = list(
                    ~1|species,
                    ~1|phylogeny,
                    ~1|pairID),
                  R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_pp3b)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 36.3442  -72.6885  -62.6885  -50.1894  -61.9742   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0080  0.0892     46     no    species   no 
sigma^2.2  0.0099  0.0996     46     no  phylogeny  yes 
sigma^2.3  0.0080  0.0892     46     no     pairID   no 

Test for Residual Heterogeneity:
QE(df = 90) = 300.4417, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 90) = 7.8110, p-val = 0.0063

Model Results:

             estimate      se    tval  df    pval    ci.lb   ci.ub     
intrcpt        0.0892  0.0667  1.3388  90  0.1840  -0.0432  0.2217     
pre_postpre    0.0539  0.0193  2.7948  90  0.0063   0.0156  0.0923  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#robust(mod_all, cluster = species)  

orchard_plot(mod_pp3, mod = "pre_post", xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-analysis: alternative (without phylogeny)

# meta-analysis

# variance-covariance matrix for sampling error assuming 0.5 correlation

mod_np2 <- rma.mv(yi, V = vi,
                  random = list(
                    ~1|species,
                    #~1|phylogeny,
                    ~1|pairID),
                  #R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_np2)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 33.1650  -66.3300  -60.3300  -52.7975  -60.0542   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2.1  0.0101  0.1006     46     no  species 
sigma^2.2  0.0101  0.1006     46     no   pairID 

Test for Heterogeneity:
Q(df = 91) = 303.2831, p-val < .0001

Model Results:

estimate      se    tval  df    pval   ci.lb   ci.ub      
  0.1162  0.0238  4.8887  91  <.0001  0.0690  0.1634  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_np2), 5)

  I2_Total I2_species  I2_pairID 
  71.77229   35.88614   35.88614 

#robust(mod_all, cluster = species)  

orchard_plot(mod_np2, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regressoin: pre vs. post (wihtout phylogeny)

mod_np3 <- rma.mv(yi, V = vi,
                  mod = ~ pre_post - 1,
                  random = list(
                    ~1|species,
                   # ~1|phylogeny,
                    ~1|pairID),
                  #R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_np3)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 35.6633  -71.3267  -63.3267  -53.3274  -62.8561   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2.1  0.0103  0.1014     46     no  species 
sigma^2.2  0.0103  0.1014     46     no   pairID 

Test for Residual Heterogeneity:
QE(df = 90) = 300.4417, p-val < .0001

Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 90) = 15.7728, p-val < .0001

Model Results:

              estimate      se    tval  df    pval   ci.lb   ci.ub      
pre_postpost    0.0895  0.0257  3.4761  90  0.0008  0.0383  0.1406  *** 
pre_postpre     0.1437  0.0258  5.5636  90  <.0001  0.0924  0.1950  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(r2_ml(mod_np3), 5)

    R2_marginal R2_conditional1 R2_conditional2 
        0.03494         0.51747         0.51747 

mod_np3b <- rma.mv(yi, V = vi,
                  mod = ~ pre_post,
                  random = list(
                    ~1|species,
                  #  ~1|phylogeny,
                    ~1|pairID),
                  #R = list(phylogeny = cor_tree),
                  data = dat_long,
                  test = "t",
                  sparse = TRUE)
summary(mod_np3b)

Multivariate Meta-Analysis Model (k = 92; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 35.6633  -71.3267  -63.3267  -53.3274  -62.8561   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2.1  0.0103  0.1014     46     no  species 
sigma^2.2  0.0103  0.1014     46     no   pairID 

Test for Residual Heterogeneity:
QE(df = 90) = 300.4417, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 90) = 7.9244, p-val = 0.0060

Model Results:

             estimate      se    tval  df    pval   ci.lb   ci.ub      
intrcpt        0.0895  0.0257  3.4761  90  0.0008  0.0383  0.1406  *** 
pre_postpre    0.0543  0.0193  2.8150  90  0.0060  0.0160  0.0925   ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#robust(mod_all, cluster = species)  

orchard_plot(mod_np3, mod = "pre_post", xlab = "lnRR (all)", group = "species", g = FALSE)

24 Extra analysis

This extra analysis compares the impact of group living and testes mass on the lifespan extension.

24.1 Loading data and effect size calculation

# loading data
dat0 <- read.csv(here("data", "zoo", "extra.csv"), na = c("", "NA"))

load(here("Rdata", "zoo", "maxCredTreeMammals.RData"))

tree <- maxCred #read.tree(here("data", "zoo", "tree_zoo.tre"))

# taxonomy
tax <- read.csv(here("data", "zoo", "species_merge_list.csv"))

dat0 %>% left_join(tax, by = c("Species" = "ZIMSSpecies")) -> dat_full

# talking out species with no data (Pseudocheirus peregrinus = likely to be mistaks in data)
# dat_full %>% filter(Species != "Chrysocyon brachyurus" &
#                     Species != "Crocuta crocuta" &
#                     Species != "Neofelis nebulosa" &
#                     Species != "Panthera uncia" &
#                     Species != "Pseudocheirus peregrinus") %>% 
#   mutate(phylogeny = gsub(" ", "_", vertlife.species)) -> dat

dat_full %>% 
  mutate(phylogeny = gsub(" ", "_", vertlife.treename)) -> dat

# take out NA which at Species column

dat <- dat[!is.na(dat$Species),]

# take out or filter out Macaca leonina (Species)

dat <- dat[dat$Species != "Macaca leonina", ]

# life span data 
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% unique(dat$phylogeny)))]

tree <- drop.tip(tree, to_drop)

# checking the number of spp
#length(tree$tip.label)

tree <- as.ultrametric(tree)

#tree <- compute.brlen(tree)
cor_tree <- vcv(tree, corr = TRUE)


missing <- which(is.na(dat$phylogeny))

dat$Species[missing]

character(0)

# creating effect size 

dat <- escalc("ROM", 
              m1i = LifeExpContra,
              m2i = LifeExpNoContra,
              sd1i = SEcontra*sqrt(Ncontra),
              sd2i = SEnoContra*sqrt(NnoContra), 
              n1i = Ncontra,
              n2i = NnoContra,
              data = dat,
)

25 Meta-analysis and meta-regression

Main

mod_extra <- rma.mv(yi, V = vi,
                 random = list(
                   ~1|species,
                   ~1|phylogeny),
                 R = list(phylogeny = cor_tree),
                 test = "t",
                 data = dat)
summary(mod_extra)

Multivariate Meta-Analysis Model (k = 75; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 27.0989  -54.1977  -48.1977  -41.2855  -47.8549   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0157  0.1252     75     no    species   no 
sigma^2.2  0.0039  0.0625     75     no  phylogeny  yes 

Test for Heterogeneity:
Q(df = 74) = 260.6402, p-val < .0001

Model Results:

estimate      se    tval  df    pval    ci.lb   ci.ub    
  0.0839  0.0450  1.8650  74  0.0661  -0.0057  0.1736  . 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(i2_ml(mod_extra), 2)

    I2_Total   I2_species I2_phylogeny 
       75.46        60.40        15.06 

#robust(mod_all, cluster = species)  

orchard_plot(mod_extra, xlab = "lnRR (all)", group = "species", g = FALSE)

Meta-regression (Group.living)

# fitting 2 moderators
mod_extra1 <- rma.mv(yi, V = vi,
                    mod = ~ Group.living - 1,
                    random = list(
                      ~1|species,
                      ~1|phylogeny),
                    R = list(phylogeny = cor_tree),
                    data = dat)
summary(mod_extra1)

Multivariate Meta-Analysis Model (k = 75; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 26.3804  -52.7607  -44.7607  -35.5989  -44.1725   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0162  0.1271     75     no    species   no 
sigma^2.2  0.0033  0.0577     75     no  phylogeny  yes 

Test for Residual Heterogeneity:
QE(df = 73) = 259.5542, p-val < .0001

Test of Moderators (coefficients 1:2):
QM(df = 2) = 4.2568, p-val = 0.1190

Model Results:

                 estimate      se    zval    pval    ci.lb   ci.ub    
Group.livingNo     0.0717  0.0493  1.4548  0.1457  -0.0249  0.1683    
Group.livingYes    0.0935  0.0455  2.0523  0.0401   0.0042  0.1827  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

orchard_plot(mod_extra1, xlab = "lnRR (all)", mod = "Group.living", group = "species", g = FALSE)

Meta-regression (lntestemass)

dat$lnbodymass <- log(dat$Bodymass)
dat$lntestemass <-log(dat$Testesmass)

mod_extra2 <- rma.mv(yi, V = vi,
                     mod = ~  lntestemass + lnbodymass,
                     random = list(
                       ~1|species,
                       ~1|phylogeny),
                     R = list(phylogeny = cor_tree),
                     data = dat)
summary(mod_extra2)

Multivariate Meta-Analysis Model (k = 59; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 28.5773  -57.1547  -47.1547  -37.0279  -45.9547   

Variance Components:

            estim    sqrt  nlvls  fixed     factor    R 
sigma^2.1  0.0114  0.1067     59     no    species   no 
sigma^2.2  0.0000  0.0000     59     no  phylogeny  yes 

Test for Residual Heterogeneity:
QE(df = 56) = 159.9245, p-val < .0001

Test of Moderators (coefficients 2:3):
QM(df = 2) = 1.8152, p-val = 0.4035

Model Results:

             estimate      se     zval    pval    ci.lb   ci.ub    
intrcpt        0.1689  0.0953   1.7720  0.0764  -0.0179  0.3558  . 
lntestemass    0.0261  0.0194   1.3468  0.1781  -0.0119  0.0641    
lnbodymass    -0.0164  0.0146  -1.1228  0.2615  -0.0450  0.0122    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

bubble_plot(mod_extra2, xlab = "log(Teste mass)", mod = "lntestemass", group = "species", g = FALSE)

26 PART IV: ANALYSIS OF CAUSES OF DEATH ON ZOO DATA

26.1 Loading data

# main data
dat0 <- read_csv(here("data", "zoo2", "causeDeathAll.csv"), na = c("", "NA"))

# turning character strings into factors
dat0 <- dat0 %>% mutate(across(where(is.character), as.factor))

# length(unique(dat0$Species)) # the number of species 131

# phylogeny
tree <- read.tree(here("data", "zoo", "tree_zoo.tre"))

# taxonomy
tax <- read.csv(here("data", "zoo", "vertlife_taxonomy_translation_table.csv"))

# there are missing species in the taxonomy table
dat0 %>% left_join(tax, by = c("Species" = "zims.species")) %>% 
  # make character strings into factors
  mutate(across(where(is.character), as.factor)) -> dat_full

dat <- dat_full


dat$Phylogeny <- gsub(" ", "_", dat$vertlife.species)

#life span data
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% unique(dat$Phylogeny)))]

tree <- drop.tip(tree, to_drop)

# checking the number of spp
#length(tree$tip.label)

tree <- compute.brlen(tree)

#tree <- compute.brlen(tree)
cor_tree <- vcv(tree, corr = TRUE)


# samller data set which does not have NA in dat$Phylogeny

dat <- dat %>% filter(!is.na(Phylogeny))

# Effect_ID is the unique identifier for the effect

dat$Effect_ID <- factor(1:nrow(dat))


# creating a variable combining Sex and Type
dat$Sex_Type <- as.factor(paste0(dat$Sex, "_", dat$Type))

26.2 Meta-analysis of risk difference

Behavioural

#########
# Behavioural
#########

# adding 0.5 for N = 0
#dat$Contra_Behavioural_N[dat$Contra_Behavioural_N == 0] <- 0.5
#dat$noContra_Behavioural_N[dat$noContra_Behavioural_N == 0] <- 0.5

# Low
dat_behavioural <- escalc(measure = "RD", 
               ai = Contra_Behavioural_Low*Contra_Behavioural_N, 
               bi = (1-Contra_Behavioural_Low)*Contra_Behavioural_N, 
               ci = noContra_Behavioural_Low*noContra_Behavioural_N,
               di = (1-noContra_Behavioural_Low)*noContra_Behavioural_N,
               var.names = c("yi_behavioural_low", "vi_behavioural_low"),
               data = dat)
# Med

dat_behavioural <- escalc(measure = "RD", 
               ai = Contra_Behavioural_Med*Contra_Behavioural_N, 
               bi = (1-Contra_Behavioural_Med)*Contra_Behavioural_N, 
               ci = noContra_Behavioural_Med*noContra_Behavioural_N,
               di = (1-noContra_Behavioural_Med)*noContra_Behavioural_N,
               var.names = c("yi_behavioural_med", "vi_behavioural_med"),
               data = dat_behavioural)

# Upp

dat_behavioural <- escalc(measure = "RD", 
               ai = Contra_Behavioural_Upp*Contra_Behavioural_N, 
               bi = (1-Contra_Behavioural_Upp)*Contra_Behavioural_N, 
               ci = noContra_Behavioural_Upp*noContra_Behavioural_N,
               di = (1-noContra_Behavioural_Upp)*noContra_Behavioural_N,
               var.names = c("yi_behavioural_upp", "vi_behavioural_upp"),
               data = dat_behavioural)

dat_behavioural %>% filter(Contra_Behavioural_N > 0) %>% filter(noContra_Behavioural_N > 0) -> dat_behavioural

# create a long format of the data  using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_behavioural <- dat_behavioural %>% select(Effect_ID, Species, Phylogeny, Sex_Type, Sex,
                           yi_behavioural_low, vi_behavioural_low, 
                           yi_behavioural_med, vi_behavioural_med, 
                           yi_behavioural_upp, vi_behavioural_upp) %>% 
  pivot_longer(cols = c(yi_behavioural_low, yi_behavioural_med, yi_behavioural_upp, vi_behavioural_low, vi_behavioural_med, vi_behavioural_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_behavioural <- dat_long_behavioural %>% filter(!is.na(yi))

str(dat_long_behavioural)

tibble [201 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 2 2 2 3 3 3 6 6 6 7 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 1 1 1 2 2 2 4 4 4 5 ...
 $ Phylogeny: chr [1:201] "Addax_nasomaculatus" "Addax_nasomaculatus" "Addax_nasomaculatus" "Aepyceros_melampus" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
 $ type     : chr [1:201] "behavioural_low" "behavioural_med" "behavioural_upp" "behavioural_low" ...
 $ yi       : num [1:201] -0.0118 -0.2489 -0.228 -0.0691 -0.2465 ...
 $ vi       : num [1:201] 0.02777 0.04229 0.05688 0.00952 0.01555 ...

dat_long_behavioural$type <- factor(dat_long_behavioural$type, levels = rev(c("behavioural_low", "behavioural_med", "behavioural_upp")),
                        labels = rev(c("Lower", "Median", "Upper")))

# effect size level ID

dat_long_behavioural$Effect_ID2 <- factor(1 : nrow(dat_long_behavioural))


# VCV

VCV <- vcalc(dat_long_behavioural$vi, 
            cluster = dat_long_behavioural$Effect_ID, 
            obs = dat_long_behavioural$Effect_ID2, 
            data = dat_long_behavioural, rho = 0.5)


# meta-analysis using dat_long

mod_behavioural <- rma.mv(yi = yi, V = VCV, 
                     random = list(
                       ~1|Species,
                       ~1|Phylogeny,
                       #~1|Effect_ID, 
                       ~1|Effect_ID2),
                     R = list(Phylogeny = cor_tree), 
                     data = dat_long_behavioural,
                     method="REML", 
                     sparse=TRUE,
                     control=list(optimizer="optim", optmethod="Nelder-Mead")
)


summary(mod_behavioural)

Multivariate Meta-Analysis Model (k = 201; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  93.8448  -187.6896  -179.6896  -166.4963  -179.4845   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     51     no     Species   no 
sigma^2.2  0.0000  0.0000     51     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    201     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 200) = 77.4901, p-val = 1.0000

Model Results:

estimate      se     zval    pval    ci.lb    ci.ub      
 -0.0567  0.0167  -3.3994  0.0007  -0.0893  -0.0240  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_behavioural_reg <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         #struct = "DIAG",
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_behavioural,
                         method="REML", 
                         control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                         mods = ~ type - 1,
                         sparse=TRUE
)

summary(mod_behavioural_reg)

Multivariate Meta-Analysis Model (k = 201; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  95.1552  -190.3104  -178.3104  -158.5808  -177.8706   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     51     no     Species   no 
sigma^2.2  0.0006  0.0241     51     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    201     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 198) = 71.0004, p-val = 1.0000

Test of Moderators (coefficients 1:3):
QM(df = 3) = 12.2076, p-val = 0.0067

Model Results:

            estimate      se     zval    pval    ci.lb    ci.ub     
typeUpper    -0.1011  0.0313  -3.2250  0.0013  -0.1625  -0.0396  ** 
typeMedian   -0.0779  0.0284  -2.7404  0.0061  -0.1336  -0.0222  ** 
typeLower    -0.0387  0.0231  -1.6770  0.0935  -0.0840   0.0065   . 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_behavioural <- orchard_plot(mod_behavioural_reg, mod = "type",
    xlab = "Risk difference (Behavioural)", group = "Species") + ylim(-0.7, 0.7)


p_behavioural

# sex_type 

dat_long_behavioural$sex_type <-  as.factor(paste0(dat_long_behavioural$Sex, "_", dat_long_behavioural$type))

mod_behavioural_reg2 <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         #struct = "DIAG",
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_behavioural,
                         method="REML", 
                         control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                         mods = ~ sex_type - 1,
                         sparse=TRUE
)

summary(mod_behavioural_reg2)

Multivariate Meta-Analysis Model (k = 201; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  94.5082  -189.0165  -171.0165  -141.5595  -170.0435   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     51     no     Species   no 
sigma^2.2  0.0008  0.0290     51     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    201     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 195) = 67.3577, p-val = 1.0000

Test of Moderators (coefficients 1:6):
QM(df = 6) = 14.8120, p-val = 0.0218

Model Results:

                       estimate      se     zval    pval    ci.lb    ci.ub     
sex_typeFemale_Lower    -0.0514  0.0353  -1.4580  0.1448  -0.1205   0.0177     
sex_typeFemale_Median   -0.0570  0.0437  -1.3054  0.1918  -0.1426   0.0286     
sex_typeFemale_Upper    -0.0562  0.0482  -1.1655  0.2438  -0.1506   0.0383     
sex_typeMale_Lower      -0.0295  0.0292  -1.0091  0.3129  -0.0867   0.0278     
sex_typeMale_Median     -0.0895  0.0362  -2.4741  0.0134  -0.1603  -0.0186   * 
sex_typeMale_Upper      -0.1284  0.0401  -3.2019  0.0014  -0.2070  -0.0498  ** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_behavioural2 <- orchard_plot(mod_behavioural_reg2, mod = "sex_type",
                         xlab = "Risk difference \n(Behavioural)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_behavioural2

# mod_table
res_behavioural <- mod_results(mod_behavioural_reg2,  mod = "sex_type", group = "Species")

attr(res_behavioural, "class") <- NULL

res_behavioural$mod_table$name <- paste( res_behavioural$mod_table$name, "Behavioural", sep = "_")
res_behavioural$mod_table$name <- factor(res_behavioural$mod_table$name)
res_behavioural$data$moderator <- paste( res_behavioural$data$moderator, "Behavioural", sep = "_")
res_behavioural$data$moderator <- factor(res_behavioural$data$moderator)

Infectious Disease

####################
# Infectious Disease
####################

# adding 0.5 for N = 0

#dat$Contra_InfectDisease_N[dat$Contra_InfectDisease_N == 0] <- 0.5
#dat$noContra_InfectDisease_N[dat$noContra_InfectDisease_N == 0] <- 0.5

# Low

dat_infectious <- escalc(measure = "RD", 
               ai = Contra_InfectDisease_Low*Contra_InfectDisease_N,
               bi = (1-Contra_InfectDisease_Low)*Contra_InfectDisease_N,
               ci = noContra_InfectDisease_Low*noContra_InfectDisease_N,
               di = (1-noContra_InfectDisease_Low)*noContra_InfectDisease_N,
               var.names = c("yi_infectious_low", "vi_infectious_low"),
               data = dat)

# Med

dat_infectious <- escalc(measure = "RD",
               ai = Contra_InfectDisease_Med*Contra_InfectDisease_N, 
               bi = (1-Contra_InfectDisease_Med)*Contra_InfectDisease_N, 
               ci = noContra_InfectDisease_Med*noContra_InfectDisease_N,
               di = (1-noContra_InfectDisease_Med)*noContra_InfectDisease_N,
               var.names = c("yi_infectious_med", "vi_infectious_med"),
               data = dat_infectious)

# Upp

dat_infectious <- escalc(measure = "RD",
               ai = Contra_InfectDisease_Upp*Contra_InfectDisease_N, 
               bi = (1-Contra_InfectDisease_Upp)*Contra_InfectDisease_N, 
               ci = noContra_InfectDisease_Upp*noContra_InfectDisease_N,
               di = (1-noContra_InfectDisease_Upp)*noContra_InfectDisease_N,
               var.names = c("yi_infectious_upp", "vi_infectious_upp"),
               data = dat_infectious)

dat_infectious %>% filter(Contra_InfectDisease_N > 0) %>%  filter(noContra_InfectDisease_N > 0) -> dat_infectious


# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_infectious <- dat_infectious %>% select(Effect_ID, Species, Phylogeny, Sex_Type, Sex,
                           yi_infectious_low, vi_infectious_low, 
                           yi_infectious_med, vi_infectious_med, 
                           yi_infectious_upp, vi_infectious_upp) %>% 
  pivot_longer(cols = c(yi_infectious_low, yi_infectious_med, yi_infectious_upp, vi_infectious_low, vi_infectious_med, vi_infectious_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_infectious <- dat_long_infectious %>% filter(!is.na(yi))

str(dat_long_infectious)

tibble [222 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 2 2 2 3 3 3 4 4 4 7 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 1 1 1 2 2 2 3 3 3 5 ...
 $ Phylogeny: chr [1:222] "Addax_nasomaculatus" "Addax_nasomaculatus" "Addax_nasomaculatus" "Aepyceros_melampus" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 4 4 4 4 4 4 1 1 1 4 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 1 1 1 2 ...
 $ type     : chr [1:222] "infectious_low" "infectious_med" "infectious_upp" "infectious_low" ...
 $ yi       : num [1:222] 0.0846 -0.0439 -0.04 0.0482 0.0101 ...
 $ vi       : num [1:222] 0.0513 0.0574 0.093 0.0104 0.0168 ...

dat_long_infectious$type <- factor(dat_long_infectious$type, 
                           levels = rev(c("infectious_low", "infectious_med", "infectious_upp")),
                           labels = rev(c("Lower", "Median", "Upper"))
)

# effect size level ID

dat_long_infectious$Effect_ID2 <- factor(1 : nrow(dat_long_infectious))

# VCV

VCV <- vcalc(dat_long_infectious$vi, 
            cluster = dat_long_infectious$Effect_ID, 
            obs = dat_long_infectious$Effect_ID2, 
            data = dat_long_infectious, rho = 0.5)


# meta-analysis using dat_long

mod_infectious <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_infectious,
                         test = "t",
                         method="REML", 
                         control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod_infectious)

Multivariate Meta-Analysis Model (k = 222; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
  98.3517  -196.7034  -188.7034  -175.1108  -188.5183   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     58     no     Species   no 
sigma^2.2  0.0000  0.0000     58     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    222     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 221) = 80.0166, p-val = 1.0000

Model Results:

estimate      se     tval   df    pval    ci.lb   ci.ub    
 -0.0021  0.0177  -0.1192  221  0.9052  -0.0370  0.0328    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_infectious_reg <- rma.mv(yi = yi, V = VCV, 
                             random = list(
                               ~1|Species,
                               ~1|Phylogeny,
                               ~1|Effect_ID2), 
                             #struct = "DIAG",
                             R = list(Phylogeny = cor_tree), 
                             data = dat_long_infectious,
                             method="REML", 
                             test = "t",
                             control=list(optimizer="optim", optmethod="Nelder-Mead"),
                             mods = ~type - 1,
                             sparse=TRUE
)

summary(mod_infectious_reg)

Multivariate Meta-Analysis Model (k = 222; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 104.3731  -208.7462  -196.7462  -176.4118  -196.3500   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     58     no     Species   no 
sigma^2.2  0.0000  0.0000     58     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    222     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 219) = 64.0554, p-val = 1.0000

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 219) = 5.3252, p-val = 0.0015

Model Results:

            estimate      se     tval   df    pval    ci.lb    ci.ub     
typeUpper    -0.0808  0.0281  -2.8713  219  0.0045  -0.1363  -0.0253  ** 
typeMedian   -0.0293  0.0235  -1.2444  219  0.2147  -0.0757   0.0171     
typeLower     0.0194  0.0191   1.0171  219  0.3102  -0.0182   0.0571     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_infectious <- orchard_plot(mod_infectious_reg, mod = "type",
    xlab = "Risk difference (Infectious Disease)", group = "Species") + ylim(-0.85, 0.7)

p_infectious

# sex_type 

dat_long_infectious$sex_type <-  as.factor(paste0(dat_long_infectious$Sex, "_", dat_long_infectious$type))

mod_infectious_reg2 <- rma.mv(yi = yi, V = VCV, 
                          random = list(
                            ~1|Species,
                            ~1|Phylogeny,
                            ~1|Effect_ID2), 
                          #struct = "DIAG",
                          R = list(Phylogeny = cor_tree), 
                          data = dat_long_infectious,
                          method="REML", 
                          control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                          mods = ~ sex_type - 1,
                          sparse=TRUE
)

summary(mod_infectious_reg2)

Multivariate Meta-Analysis Model (k = 222; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 105.1417  -210.2834  -192.2834  -161.9059  -191.4096   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     58     no     Species   no 
sigma^2.2  0.0000  0.0000     58     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    222     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 216) = 57.8647, p-val = 1.0000

Test of Moderators (coefficients 1:6):
QM(df = 6) = 22.1661, p-val = 0.0011

Model Results:

                       estimate      se     zval    pval    ci.lb    ci.ub     
sex_typeFemale_Lower     0.0145  0.0317   0.4567  0.6479  -0.0477   0.0766     
sex_typeFemale_Median   -0.0861  0.0368  -2.3422  0.0192  -0.1581  -0.0140   * 
sex_typeFemale_Upper    -0.1344  0.0421  -3.1892  0.0014  -0.2170  -0.0518  ** 
sex_typeMale_Lower       0.0253  0.0240   1.0548  0.2915  -0.0217   0.0723     
sex_typeMale_Median      0.0124  0.0307   0.4053  0.6853  -0.0477   0.0726     
sex_typeMale_Upper      -0.0367  0.0379  -0.9688  0.3327  -0.1110   0.0376     

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_infectious2 <- orchard_plot(mod_infectious_reg2, mod = "sex_type",
                          xlab = "Risk difference \n(Infectious Disease)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_infectious2

# mod_table
res_infectious <- mod_results(mod_infectious_reg2,  mod = "sex_type", group = "Species")

attr(res_infectious, "class") <- NULL

res_infectious$mod_table$name <- paste(res_infectious$mod_table$name, "Infectious", sep = "_")
res_infectious$mod_table$name <- factor(res_infectious$mod_table$name)
res_infectious$data$moderator <- paste(res_infectious$data$moderator, "Infectious", sep = "_")
res_infectious$data$moderator <- factor(res_infectious$data$moderator)

Non-infectious Disease

########################
# Non-infectious Disease
########################

# adding 0.5 for N = 0

#dat$Contra_NonInfectDisease_N[dat$Contra_NonInfectDisease_N == 0] <- 0.5
#dat$noContra_NonInfectDisease_N[dat$noContra_NonInfectDisease_N == 0] <- 0.5

# Low

dat_noninfectious <- escalc(measure = "RD", 
               ai = Contra_NonInfectDisease_Low*Contra_NonInfectDisease_N,
               bi = (1-Contra_NonInfectDisease_Low)*Contra_NonInfectDisease_N,
               ci = noContra_NonInfectDisease_Low*noContra_NonInfectDisease_N,
               di = (1-noContra_NonInfectDisease_Low)*noContra_NonInfectDisease_N,
               var.names = c("yi_noninfectious_low", "vi_noninfectious_low"),
               data = dat)

# Med

dat_noninfectious <- escalc(measure = "RD",
               ai = Contra_NonInfectDisease_Med*Contra_NonInfectDisease_N, 
               bi = (1-Contra_NonInfectDisease_Med)*Contra_NonInfectDisease_N, 
               ci = noContra_NonInfectDisease_Med*noContra_NonInfectDisease_N,
               di = (1-noContra_NonInfectDisease_Med)*noContra_NonInfectDisease_N,
               var.names = c("yi_noninfectious_med", "vi_noninfectious_med"),
               data = dat_noninfectious)

# Upp

dat_noninfectious <- escalc(measure = "RD",
               ai = Contra_NonInfectDisease_Upp*Contra_NonInfectDisease_N, 
               bi = (1-Contra_NonInfectDisease_Upp)*Contra_NonInfectDisease_N, 
               ci = noContra_NonInfectDisease_Upp*noContra_NonInfectDisease_N,
               di = (1-noContra_NonInfectDisease_Upp)*noContra_NonInfectDisease_N,
               var.names = c("yi_noninfectious_upp", "vi_noninfectious_upp"),
               data = dat_noninfectious)

dat_noninfectious %>% filter(Contra_NonInfectDisease_N > 0) %>% filter(noContra_NonInfectDisease_N > 0) -> dat_noninfectious

# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_noninfectious <- dat_noninfectious %>% select(Effect_ID, Species, Phylogeny, Sex_Type, Sex,
                           yi_noninfectious_low, vi_noninfectious_low, 
                           yi_noninfectious_med, vi_noninfectious_med, 
                           yi_noninfectious_upp, vi_noninfectious_upp) %>% 
  pivot_longer(cols = c(yi_noninfectious_low, yi_noninfectious_med, yi_noninfectious_upp, vi_noninfectious_low, vi_noninfectious_med, vi_noninfectious_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_noninfectious <- dat_long_noninfectious %>% filter(!is.na(yi))

str(dat_long_noninfectious)

tibble [276 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 2 2 2 4 4 4 6 6 6 7 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 1 1 1 3 3 3 4 4 4 5 ...
 $ Phylogeny: chr [1:276] "Addax_nasomaculatus" "Addax_nasomaculatus" "Addax_nasomaculatus" "Alouatta_caraya" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 4 4 4 1 1 1 4 4 4 4 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 1 2 2 2 2 ...
 $ type     : chr [1:276] "noninfectious_low" "noninfectious_med" "noninfectious_upp" "noninfectious_low" ...
 $ yi       : num [1:276] 0.0694 0.0509 -0.0255 0.0773 0.0773 ...
 $ vi       : num [1:276] 0.0332 0.035 0.0352 0.1223 0.1223 ...

dat_long_noninfectious$type <- factor(dat_long_noninfectious$type, 
                           levels = rev(c("noninfectious_low", "noninfectious_med", "noninfectious_upp")),
                           labels = rev(c("Lower", "Median", "Upper"))
                           )

# effect size level ID

dat_long_noninfectious$Effect_ID2 <- factor(1 : nrow(dat_long_noninfectious))

# VCV

VCV <- vcalc(dat_long_noninfectious$vi, 
            cluster = dat_long_noninfectious$Effect_ID, 
            obs = dat_long_noninfectious$Effect_ID2, 
            data = dat_long_noninfectious, rho = 0.5)

# meta-analysis using dat_long

mod_noninfectious <- rma.mv(yi = yi, V = VCV, 
                           random = list(
                             ~1|Species,
                             ~1|Phylogeny,
                             ~1|Effect_ID2), 
                           R = list(Phylogeny = cor_tree), 
                           data = dat_long_noninfectious,
                           method="REML", 
                           test = "t",
                           control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod_noninfectious)

Multivariate Meta-Analysis Model (k = 276; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 184.0053  -368.0107  -360.0107  -345.5436  -359.8625   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     67     no     Species   no 
sigma^2.2  0.0000  0.0000     67     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    276     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 275) = 135.6728, p-val = 1.0000

Model Results:

estimate      se    tval   df    pval    ci.lb   ci.ub    
  0.0059  0.0098  0.6013  275  0.5482  -0.0134  0.0251    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_noninfectious_reg <- rma.mv(yi = yi, V = VCV, 
                               random = list(
                                 ~1|Species,
                                 ~1|Phylogeny,
                                 ~1|Effect_ID2), 
                               #struct = "DIAG",
                               R = list(Phylogeny = cor_tree), 
                               data = dat_long_noninfectious,
                               method="REML", 
                               control=list(optimizer="optim", optmethod="Nelder-Mead"),
                               mods = ~type - 1,
                               test = "t",
                               sparse=TRUE
)

summary(mod_noninfectious_reg)

Multivariate Meta-Analysis Model (k = 276; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 183.4484  -366.8968  -354.8968  -333.2399  -354.5810   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     67     no     Species   no 
sigma^2.2  0.0000  0.0000     67     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    276     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 273) = 131.5937, p-val = 1.0000

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 273) = 1.4802, p-val = 0.2202

Model Results:

            estimate      se     tval   df    pval    ci.lb   ci.ub    
typeUpper    -0.0084  0.0205  -0.4086  273  0.6831  -0.0486  0.0319    
typeMedian   -0.0145  0.0142  -1.0232  273  0.3071  -0.0424  0.0134    
typeLower     0.0110  0.0102   1.0865  273  0.2782  -0.0090  0.0311    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_noninfectious <- orchard_plot(mod_noninfectious_reg, mod = "type",
    xlab = "Risk difference (Non-infectious Disease)", group = "Species") + ylim(-0.85, 0.7)

p_noninfectious

# sex_type 

dat_long_noninfectious$sex_type <-  as.factor(paste0(dat_long_noninfectious$Sex, "_", dat_long_noninfectious$type))

mod_noninfectious_reg2 <- rma.mv(yi = yi, V = VCV, 
                              random = list(
                                ~1|Species,
                                ~1|Phylogeny,
                                ~1|Effect_ID2), 
                              #struct = "DIAG",
                              R = list(Phylogeny = cor_tree), 
                              data = dat_long_noninfectious,
                              method="REML", 
                              control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                              mods = ~ sex_type - 1,
                              sparse=TRUE
)

summary(mod_noninfectious_reg2)

Multivariate Meta-Analysis Model (k = 276; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 181.8799  -363.7597  -345.7597  -313.3739  -345.0674   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     67     no     Species   no 
sigma^2.2  0.0000  0.0000     67     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    276     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 270) = 127.7237, p-val = 1.0000

Test of Moderators (coefficients 1:6):
QM(df = 6) = 8.3106, p-val = 0.2162

Model Results:

                       estimate      se     zval    pval    ci.lb   ci.ub    
sex_typeFemale_Lower     0.0121  0.0150   0.8067  0.4198  -0.0174  0.0416    
sex_typeFemale_Median   -0.0364  0.0227  -1.6070  0.1080  -0.0808  0.0080    
sex_typeFemale_Upper    -0.0428  0.0309  -1.3857  0.1658  -0.1034  0.0178    
sex_typeMale_Lower       0.0094  0.0138   0.6795  0.4968  -0.0177  0.0364    
sex_typeMale_Median     -0.0007  0.0182  -0.0367  0.9707  -0.0363  0.0349    
sex_typeMale_Upper       0.0170  0.0273   0.6226  0.5336  -0.0365  0.0705    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_noninfectious2 <- orchard_plot(mod_noninfectious_reg2, mod = "sex_type",
                              xlab = "Risk difference \n(None-infectious Disease)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_noninfectious2

# mod_table
res_noninfectious <- mod_results(mod_noninfectious_reg2,  mod = "sex_type", group = "Species")

attr(res_noninfectious, "class") <- NULL

res_noninfectious$mod_table$name <- paste(res_noninfectious$mod_table$name, "None-infectious", sep = "_")
res_noninfectious$mod_table$name <- factor(res_noninfectious$mod_table$name)
res_noninfectious$data$moderator <- paste(res_noninfectious$data$moderator, "None-infectious", sep = "_")
res_noninfectious$data$moderator <- factor(res_noninfectious$data$moderator)

Chronic Disease

###################
# Chronic Disease
###################

# adding 0.5 for N = 0
#dat$Contra_ChronDisease_N[dat$Contra_ChronDisease_N == 0] <- 0.5
#dat$noContra_ChronDisease_N[dat$noContra_ChronDisease_N == 0] <- 0.5

# Low

dat_chronic <- escalc(measure = "RD", 
               ai = Contra_ChronDisease_Low*Contra_ChronDisease_N,
               bi = (1-Contra_ChronDisease_Low)*Contra_ChronDisease_N,
               ci = noContra_ChronDisease_Low*noContra_ChronDisease_N,
               di = (1-noContra_ChronDisease_Low)*noContra_ChronDisease_N,
               var.names = c("yi_chronic_low", "vi_chronic_low"),
               data = dat)

# Med

dat_chronic <- escalc(measure = "RD",
               ai = Contra_ChronDisease_Med*Contra_ChronDisease_N, 
               bi = (1-Contra_ChronDisease_Med)*Contra_ChronDisease_N, 
               ci = noContra_ChronDisease_Med*noContra_ChronDisease_N,
               di = (1-noContra_ChronDisease_Med)*noContra_ChronDisease_N,
               var.names = c("yi_chronic_med", "vi_chronic_med"),
               data = dat_chronic)

# Upp

dat_chronic <- escalc(measure = "RD",
               ai = Contra_ChronDisease_Upp*Contra_ChronDisease_N, 
               bi = (1-Contra_ChronDisease_Upp)*Contra_ChronDisease_N, 
               ci = noContra_ChronDisease_Upp*noContra_ChronDisease_N,
               di = (1-noContra_ChronDisease_Upp)*noContra_ChronDisease_N,
               var.names = c("yi_chronic_upp", "vi_chronic_upp"),
               data = dat_chronic)

dat_chronic %>% filter(Contra_ChronDisease_N > 0) %>% filter(noContra_ChronDisease_N > 0) -> dat_chronic

# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_chronic <- dat_chronic %>% select(Effect_ID, Species, Phylogeny, Sex_Type,Sex,
                           yi_chronic_low, vi_chronic_low, 
                           yi_chronic_med, vi_chronic_med, 
                           yi_chronic_upp, vi_chronic_upp) %>% 
  pivot_longer(cols = c(yi_chronic_low, yi_chronic_med, yi_chronic_upp, vi_chronic_low, vi_chronic_med, vi_chronic_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_chronic <- dat_long_chronic %>% filter(!is.na(yi))

str(dat_long_chronic)

tibble [231 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 2 2 2 4 4 4 6 6 6 7 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 1 1 1 3 3 3 4 4 4 5 ...
 $ Phylogeny: chr [1:231] "Addax_nasomaculatus" "Addax_nasomaculatus" "Addax_nasomaculatus" "Alouatta_caraya" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 4 4 4 1 1 1 4 4 4 4 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 1 2 2 2 2 ...
 $ type     : chr [1:231] "chronic_low" "chronic_med" "chronic_upp" "chronic_low" ...
 $ yi       : num [1:231] 0.15 0.1083 0.0529 -0.0833 0.0779 ...
 $ vi       : num [1:231] 0.112 0.118 0.143 0.14 0.208 ...

dat_long_chronic$type <- factor(dat_long_chronic$type, 
                           levels = rev(c("chronic_low", "chronic_med", "chronic_upp")),
                           labels = rev(c("Lower", "Median", "Upper"))
                           )

# effect size level ID

dat_long_chronic$Effect_ID2 <- factor(1 : nrow(dat_long_chronic))

# VCV

VCV <- vcalc(dat_long_chronic$vi, 
            cluster = dat_long_chronic$Effect_ID, 
            obs = dat_long_chronic$Effect_ID2, 
            data = dat_long_chronic, rho = 0.5)

# meta-analysis using dat_long

mod_chronic <- rma.mv(yi = yi, V = VCV, 
                     random = list(
                       ~1|Species,
                       ~1|Phylogeny,
                       ~1|Effect_ID2), 
                     R = list(Phylogeny = cor_tree), 
                     data = dat_long_chronic,
                     method="REML", 
                     test = "t",
                     dfs = "contain",
                     control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod_chronic)

Multivariate Meta-Analysis Model (k = 231; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 115.5270  -231.0539  -223.0539  -209.3016  -222.8761   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     60     no     Species   no 
sigma^2.2  0.0000  0.0000     60     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    231     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 230) = 81.3545, p-val = 1.0000

Model Results:

estimate      se    tval  df    pval    ci.lb   ci.ub    
  0.0209  0.0159  1.3158  59  0.1933  -0.0109  0.0527    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_chronic_reg <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         struct = "DIAG",
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_chronic,
                         method="REML", 
                         control=list(optimizer="optim", optmethod="Nelder-Mead"),
                         mods = ~type - 1,
                         test = "t",
                         sparse=TRUE
)

summary(mod_chronic_reg)

Multivariate Meta-Analysis Model (k = 231; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 114.9271  -229.8541  -217.8541  -197.2781  -217.4740   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     60     no     Species   no 
sigma^2.2  0.0000  0.0000     60     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    231     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 228) = 78.2929, p-val = 1.0000

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 228) = 1.5977, p-val = 0.1907

Model Results:

            estimate      se    tval   df    pval    ci.lb   ci.ub    
typeUpper     0.0237  0.0267  0.8860  228  0.3765  -0.0290  0.0763    
typeMedian    0.0036  0.0187  0.1901  228  0.8494  -0.0333  0.0404    
typeLower     0.0367  0.0183  1.9993  228  0.0468   0.0005  0.0728  * 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_chronic <- orchard_plot(mod_chronic_reg, mod = "type",
    xlab = "Risk difference (Chronic Disease)", group = "Species") + ylim(-0.85, 0.7)

p_chronic

# sex_type 

dat_long_chronic$sex_type <-  as.factor(paste0(dat_long_chronic$Sex, "_", dat_long_chronic$type))

mod_chronic_reg2 <- rma.mv(yi = yi, V = VCV, 
                                 random = list(
                                   ~1|Species,
                                   ~1|Phylogeny,
                                   ~1|Effect_ID2), 
                                 #struct = "DIAG",
                                 R = list(Phylogeny = cor_tree), 
                                 data = dat_long_chronic,
                                 method="REML", 
                                 control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                                 mods = ~ sex_type - 1,
                                 sparse=TRUE
)

summary(mod_chronic_reg2)

Multivariate Meta-Analysis Model (k = 231; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 112.3685  -224.7370  -206.7370  -175.9921  -205.8998   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000     60     no     Species   no 
sigma^2.2  0.0000  0.0000     60     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000    231     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 225) = 78.1638, p-val = 1.0000

Test of Moderators (coefficients 1:6):
QM(df = 6) = 4.9222, p-val = 0.5538

Model Results:

                       estimate      se     zval    pval    ci.lb   ci.ub    
sex_typeFemale_Lower     0.0332  0.0284   1.1711  0.2415  -0.0224  0.0888    
sex_typeFemale_Median   -0.0047  0.0301  -0.1546  0.8771  -0.0636  0.0543    
sex_typeFemale_Upper     0.0206  0.0434   0.4741  0.6354  -0.0644  0.1055    
sex_typeMale_Lower       0.0390  0.0241   1.6197  0.1053  -0.0082  0.0861    
sex_typeMale_Median      0.0088  0.0239   0.3668  0.7138  -0.0381  0.0556    
sex_typeMale_Upper       0.0256  0.0339   0.7539  0.4509  -0.0409  0.0920    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_chronic2 <- orchard_plot(mod_chronic_reg2, mod = "sex_type",
                                 xlab = "Risk difference \n(Chronic Disease)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_chronic2

# mod_table
res_chronic <- mod_results(mod_chronic_reg2,  mod = "sex_type", group = "Species")

attr(res_chronic, "class") <- NULL

res_chronic$mod_table$name <- paste(res_chronic$mod_table$name, "Chronic", sep = "_")
res_chronic$mod_table$name <- factor(res_chronic$mod_table$name)
res_chronic$data$moderator <- paste(res_chronic$data$moderator, "Chronic", sep = "_")
res_chronic$data$moderator <- factor(res_chronic$data$moderator)

Death at birth

###################
# Death at birth
###################

# adding 0.5 for N = 0
#dat$Contra_deathAtBirth_N[dat$Contra_deathAtBirth_N == 0] <- 0.5
#dat$noContra_deathAtBirth_N[dat$noContra_deathAtBirth_N == 0] <- 0.5

# Low

dat_deathAtBirth <- escalc(measure = "RD", 
               ai = Contra_deathAtBirth_Low*Contra_deathAtBirth_N,
               bi = (1-Contra_deathAtBirth_Low)*Contra_deathAtBirth_N,
               ci = noContra_deathAtBirth_Low*noContra_deathAtBirth_N,
               di = (1-noContra_deathAtBirth_Low)*noContra_deathAtBirth_N,
               var.names = c("yi_deathAtBirth_low", "vi_deathAtBirth_low"),
               data = dat)

# Med

dat_deathAtBirth <- escalc(measure = "RD",
               ai = Contra_deathAtBirth_Med*Contra_deathAtBirth_N, 
               bi = (1-Contra_deathAtBirth_Med)*Contra_deathAtBirth_N, 
               ci = noContra_deathAtBirth_Med*noContra_deathAtBirth_N,
               di = (1-noContra_deathAtBirth_Med)*noContra_deathAtBirth_N,
               var.names = c("yi_deathAtBirth_med", "vi_deathAtBirth_med"),
               data = dat_deathAtBirth)

# Upp

dat_deathAtBirth <- escalc(measure = "RD",
               ai = Contra_deathAtBirth_Upp*Contra_deathAtBirth_N, 
               bi = (1-Contra_deathAtBirth_Upp)*Contra_deathAtBirth_N, 
               ci = noContra_deathAtBirth_Upp*noContra_deathAtBirth_N,
               di = (1-noContra_deathAtBirth_Upp)*noContra_deathAtBirth_N,
               var.names = c("yi_deathAtBirth_upp", "vi_deathAtBirth_upp"),
               data = dat_deathAtBirth)

dat_deathAtBirth %>% filter(Contra_deathAtBirth_N > 0) %>%  filter(noContra_deathAtBirth_N > 0) -> dat_deathAtBirth


# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_deathAtBirth <- dat_deathAtBirth %>% select(Effect_ID, Species, Phylogeny, Sex_Type,Sex,
                           yi_deathAtBirth_low, vi_deathAtBirth_low, 
                           yi_deathAtBirth_med, vi_deathAtBirth_med, 
                           yi_deathAtBirth_upp, vi_deathAtBirth_upp) %>% 
  pivot_longer(cols = c(yi_deathAtBirth_low, yi_deathAtBirth_med, yi_deathAtBirth_upp, vi_deathAtBirth_low, vi_deathAtBirth_med, vi_deathAtBirth_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_deathAtBirth <- dat_long_deathAtBirth %>% filter(!is.na(yi))

str(dat_long_deathAtBirth)

tibble [18 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 17 17 17 19 19 19 67 67 67 129 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 14 14 14 16 16 16 53 53 53 95 ...
 $ Phylogeny: chr [1:18] "Callimico_goeldii" "Callimico_goeldii" "Callimico_goeldii" "Callithrix_jacchus" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 1 1 1 1 1 ...
 $ type     : chr [1:18] "deathAtBirth_low" "deathAtBirth_med" "deathAtBirth_upp" "deathAtBirth_low" ...
 $ yi       : num [1:18] 0.19385 0.070647 0.000914 0.209016 -0.001908 ...
 $ vi       : num [1:18] 0.0986 0.1476 0.1527 0.0964 0.0447 ...

dat_long_deathAtBirth$type <- factor(dat_long_deathAtBirth$type, 
                           levels = rev(c("deathAtBirth_low", "deathAtBirth_med", "deathAtBirth_upp")),
                           labels = rev(c("Lower", "Median", "Upper"))
                           )

# effect size level ID

dat_long_deathAtBirth$Effect_ID2 <- factor(1 : nrow(dat_long_deathAtBirth))

# VCV

VCV <- vcalc(dat_long_deathAtBirth$vi, 
            cluster = dat_long_deathAtBirth$Effect_ID, 
            obs = dat_long_deathAtBirth$Effect_ID2, 
            data = dat_long_deathAtBirth, rho = 0.5)

# meta-analysis using dat_long

mod_deathAtBirth <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_deathAtBirth,
                         method="REML", 
                         test = "t",
                         control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod_deathAtBirth)

Multivariate Meta-Analysis Model (k = 18; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  3.9616   -7.9232    0.0768    3.4097    3.4101   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000      6     no     Species   no 
sigma^2.2  0.0000  0.0000      6     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000     18     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 17) = 2.6813, p-val = 1.0000

Model Results:

estimate      se     tval  df    pval    ci.lb   ci.ub    
 -0.0010  0.1008  -0.0099  17  0.9922  -0.2136  0.2116    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_deathAtBirth_reg <- rma.mv(yi = yi, V = VCV, 
                             random = list(
                               ~1|Species,
                               ~1|Phylogeny,
                               ~1|Effect_ID2), 
                             #struct = "DIAG",
                             R = list(Phylogeny = cor_tree), 
                             data = dat_long_deathAtBirth,
                             method="REML", 
                             control=list(optimizer="optim", optmethod="Nelder-Mead"),
                             mods = ~type - 1,
                             test = "t",
                             sparse=FALSE
)

summary(mod_deathAtBirth_reg)

Multivariate Meta-Analysis Model (k = 18; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  3.5523   -7.1045    4.8955    9.1438   15.3955   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000      6     no     Species   no 
sigma^2.2  0.0000  0.0000      6     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000     18     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 15) = 1.0871, p-val = 1.0000

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 15) = 0.5314, p-val = 0.6676

Model Results:

            estimate      se     tval  df    pval    ci.lb   ci.ub    
typeUpper    -0.0691  0.1246  -0.5548  15  0.5872  -0.3346  0.1964    
typeMedian   -0.0202  0.1235  -0.1633  15  0.8725  -0.2833  0.2430    
typeLower     0.0873  0.1249   0.6992  15  0.4952  -0.1788  0.3535    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_deathAtBirth <- orchard_plot(mod_deathAtBirth_reg, mod = "type",
    xlab = "Risk difference (Death at Birth)", group = "Species") + ylim(-0.85, 0.7)

p_deathAtBirth

# sex_type 

dat_long_deathAtBirth$sex_type <-  as.factor(paste0(dat_long_deathAtBirth$Sex, "_", dat_long_deathAtBirth$type))

mod_deathAtBirth_reg2 <- rma.mv(yi = yi, V = VCV, 
                           random = list(
                             ~1|Species,
                             ~1|Phylogeny,
                             ~1|Effect_ID2), 
                           #struct = "DIAG",
                           R = list(Phylogeny = cor_tree), 
                           data = dat_long_deathAtBirth,
                           method="REML", 
                           control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                           mods = ~ sex_type - 1,
                           sparse=TRUE
)

summary(mod_deathAtBirth_reg2)

Multivariate Meta-Analysis Model (k = 18; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
  3.5523   -7.1045    4.8955    9.1438   15.3955   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0000  0.0000      6     no     Species   no 
sigma^2.2  0.0000  0.0000      6     no   Phylogeny  yes 
sigma^2.3  0.0000  0.0000     18     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 15) = 1.0871, p-val = 1.0000

Test of Moderators (coefficients 1:3):
QM(df = 3) = 1.5943, p-val = 0.6607

Model Results:

                       estimate      se     zval    pval    ci.lb   ci.ub    
sex_typeFemale_Lower     0.0873  0.1249   0.6992  0.4845  -0.1574  0.3320    
sex_typeFemale_Median   -0.0202  0.1235  -0.1633  0.8703  -0.2622  0.2218    
sex_typeFemale_Upper    -0.0691  0.1246  -0.5548  0.5790  -0.3133  0.1750    

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# mod_table
res_deathAtBirth <- mod_results(mod_deathAtBirth_reg2,  mod = "sex_type", group = "Species")

attr(res_deathAtBirth, "class") <- NULL

res_deathAtBirth$mod_table$name <- paste(res_deathAtBirth$mod_table$name, "Death", sep = "_")
res_deathAtBirth$mod_table$name <- factor(res_deathAtBirth$mod_table$name)
res_deathAtBirth$data$moderator <- paste(res_deathAtBirth$data$moderator, "Death", sep = "_")
res_deathAtBirth$data$moderator <- factor(res_deathAtBirth$data$moderator)


p_deathAtBirth2 <- orchard_plot(mod_deathAtBirth_reg2, mod = "sex_type",
                           xlab = "Risk difference \n(Death at birth)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_deathAtBirth2

Other

########
# Other
########

# adding 0.5 for N = 0
#dat$Contra_Other_N[dat$Contra_Other_N == 0] <- 0.5
#dat$noContra_Other_N[dat$noContra_Other_N == 0] <- 0.5

# Low

dat_other <- escalc(measure = "RD", 
               ai = Contra_Other_Low*Contra_Other_N,
               bi = (1-Contra_Other_Low)*Contra_Other_N,
               ci = noContra_Other_Low*noContra_Other_N,
               di = (1-noContra_Other_Low)*noContra_Other_N,
               var.names = c("yi_other_low", "vi_other_low"),
               data = dat)

# Med

dat_other <- escalc(measure = "RD",
               ai = Contra_Other_Med*Contra_Other_N, 
               bi = (1-Contra_Other_Med)*Contra_Other_N, 
               ci = noContra_Other_Med*noContra_Other_N,
               di = (1-noContra_Other_Med)*noContra_Other_N,
               var.names = c("yi_other_med", "vi_other_med"),
               data = dat_other)

# Upp

dat_other <- escalc(measure = "RD",
               ai = Contra_Other_Upp*Contra_Other_N, 
               bi = (1-Contra_Other_Upp)*Contra_Other_N, 
               ci = noContra_Other_Upp*noContra_Other_N,
               di = (1-noContra_Other_Upp)*noContra_Other_N,
               var.names = c("yi_other_upp", "vi_other_upp"),
               data = dat_other)

dat_other %>% filter(Contra_Other_N > 0) %>% filter(noContra_Other_N > 0) -> dat_other

# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect size

dat_long_other <- dat_other %>% select(Effect_ID, Species, Phylogeny, Sex_Type,Sex,
                           yi_other_low, vi_other_low, 
                           yi_other_med, vi_other_med, 
                           yi_other_upp, vi_other_upp) %>% 
  pivot_longer(cols = c(yi_other_low, yi_other_med, yi_other_upp, vi_other_low, vi_other_med, vi_other_upp), 
               names_to = c(".value", "type"), 
               names_pattern = "(yi|vi)_(.*)")

dat_long_other <- dat_long_other %>% filter(!is.na(yi))

str(dat_long_other)

tibble [291 × 8] (S3: tbl_df/tbl/data.frame)
 $ Effect_ID: Factor w/ 184 levels "1","2","3","4",..: 2 2 2 3 3 3 4 4 4 6 ...
 $ Species  : Factor w/ 131 levels "Addax nasomaculatus",..: 1 1 1 2 2 2 3 3 3 4 ...
 $ Phylogeny: chr [1:291] "Addax_nasomaculatus" "Addax_nasomaculatus" "Addax_nasomaculatus" "Aepyceros_melampus" ...
 $ Sex_Type : Factor w/ 4 levels "Female_Hormonal",..: 4 4 4 4 4 4 1 1 1 4 ...
 $ Sex      : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 1 1 1 2 ...
 $ type     : chr [1:291] "other_low" "other_med" "other_upp" "other_low" ...
 $ yi       : num [1:291] -0.322 -0.454 -0.277 -0.176 -0.418 ...
 $ vi       : num [1:291] 0.01039 0.01734 0.01927 0.00323 0.0051 ...

dat_long_other$type <- factor(dat_long_other$type, 
                           levels = rev(c("other_low", "other_med", "other_upp")),
                           labels = rev(c("Lower", "Median", "Upper"))
                           )
# effect size level ID  

dat_long_other$Effect_ID2 <- factor(1 : nrow(dat_long_other))

# VCV

VCV <- vcalc(dat_long_other$vi, 
            cluster = dat_long_other$Effect_ID, 
            obs = dat_long_other$Effect_ID2, 
            data = dat_long_other, rho = 0.5)


# meta-analysis using dat_long

mod_other <- rma.mv(yi = yi, V = VCV, 
                     random = list(
                       ~1|Species,
                       ~1|Phylogeny,
                       ~1|Effect_ID2), 
                     R = list(Phylogeny = cor_tree), 
                     data = dat_long_other,
                     method="REML", 
                     test = "t",
                     control=list(optimizer="optim", optmethod="Nelder-Mead")
)

summary(mod_other)

Multivariate Meta-Analysis Model (k = 291; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 123.1042  -246.2084  -238.2084  -223.5289  -238.0681   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0047  0.0683     70     no     Species   no 
sigma^2.2  0.0015  0.0386     70     no   Phylogeny  yes 
sigma^2.3  0.0137  0.1170    291     no  Effect_ID2   no 

Test for Heterogeneity:
Q(df = 290) = 1802.6136, p-val < .0001

Model Results:

estimate      se     tval   df    pval    ci.lb    ci.ub      
 -0.2357  0.0250  -9.4262  290  <.0001  -0.2849  -0.1864  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# meta-regression

mod_other_reg <- rma.mv(yi = yi, V = VCV, 
                         random = list(
                           ~1|Species,
                           ~1|Phylogeny,
                           ~1|Effect_ID2), 
                         #struct = "DIAG",
                         R = list(Phylogeny = cor_tree), 
                         data = dat_long_other,
                         method="REML", 
                         control=list(optimizer="optim", optmethod="Nelder-Mead"),
                         mods = ~type - 1,
                         test = "t",
                         sparse=FALSE
)

summary(mod_other_reg)

Multivariate Meta-Analysis Model (k = 291; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 143.0236  -286.0471  -274.0471  -252.0694  -273.7482   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0046  0.0676     70     no     Species   no 
sigma^2.2  0.0016  0.0400     70     no   Phylogeny  yes 
sigma^2.3  0.0099  0.0996    291     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 288) = 1132.1735, p-val < .0001

Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 288) = 45.9173, p-val < .0001

Model Results:

            estimate      se      tval   df    pval    ci.lb    ci.ub      
typeUpper    -0.1715  0.0275   -6.2360  288  <.0001  -0.2256  -0.1174  *** 
typeMedian   -0.3049  0.0278  -10.9838  288  <.0001  -0.3595  -0.2503  *** 
typeLower    -0.2352  0.0268   -8.7890  288  <.0001  -0.2878  -0.1825  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_other <- orchard_plot(mod_other_reg, mod = "type",
    xlab = "Risk difference (Other)", group = "Species") + ylim(-0.85, 0.7)

p_other

# sex_type 

dat_long_other$sex_type <-  as.factor(paste0(dat_long_other$Sex, "_", dat_long_other$type))

mod_other_reg2 <- rma.mv(yi = yi, V = VCV, 
                           random = list(
                             ~1|Species,
                             ~1|Phylogeny,
                             ~1|Effect_ID2), 
                           #struct = "DIAG",
                           R = list(Phylogeny = cor_tree), 
                           data = dat_long_other,
                           method="REML", 
                           control=list(optimizer="optim", optmethod= "Nelder-Mead"),
                           mods = ~ sex_type - 1,
                           sparse=TRUE
)

summary(mod_other_reg2)

Multivariate Meta-Analysis Model (k = 291; method: REML)

   logLik   Deviance        AIC        BIC       AICc   
 140.6424  -281.2847  -263.2847  -230.4123  -262.6302   

Variance Components:

            estim    sqrt  nlvls  fixed      factor    R 
sigma^2.1  0.0040  0.0636     70     no     Species   no 
sigma^2.2  0.0025  0.0496     70     no   Phylogeny  yes 
sigma^2.3  0.0101  0.1004    291     no  Effect_ID2   no 

Test for Residual Heterogeneity:
QE(df = 285) = 1118.7993, p-val < .0001

Test of Moderators (coefficients 1:6):
QM(df = 6) = 117.0718, p-val < .0001

Model Results:

                       estimate      se     zval    pval    ci.lb    ci.ub      
sex_typeFemale_Lower    -0.2501  0.0361  -6.9347  <.0001  -0.3208  -0.1794  *** 
sex_typeFemale_Median   -0.3205  0.0385  -8.3347  <.0001  -0.3959  -0.2451  *** 
sex_typeFemale_Upper    -0.1956  0.0380  -5.1409  <.0001  -0.2701  -0.1210  *** 
sex_typeMale_Lower      -0.2296  0.0325  -7.0561  <.0001  -0.2934  -0.1658  *** 
sex_typeMale_Median     -0.2991  0.0337  -8.8713  <.0001  -0.3652  -0.2330  *** 
sex_typeMale_Upper      -0.1607  0.0333  -4.8299  <.0001  -0.2259  -0.0955  *** 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p_other2 <- orchard_plot(mod_other_reg2, mod = "sex_type",
                           xlab = "Risk difference \n(Other causes)", group = "Species", flip = F) + ylim(-0.85, 0.7)


p_other2

26.3 Figure code

# mod_table
res_other <- mod_results(mod_other_reg2,  mod = "sex_type", group = "Species")

attr(res_other, "class") <- NULL

res_other$mod_table$name <- paste(res_other$mod_table$name, "Other", sep = "_")
res_other$mod_table$name <- factor(res_other$mod_table$name)
res_other$data$moderator <- paste(res_other$data$moderator, "Other", sep = "_")
res_other$data$moderator <- factor(res_other$data$moderator)

# combining all plots - use cowplot

p_all <- plot_grid(p_behavioural, 
                   p_infectious, 
                   p_noninfectious, 
                   p_chronic, 
                   p_deathAtBirth, 
                   p_other,
                   ncol = 2)

p_all

p_all2 <- plot_grid(p_behavioural2, 
                   p_infectious2, 
                   p_noninfectious2, 
                   p_chronic2, 
                   #p_deathAtBirth, 
                   p_other2,
                   ncol = 2)

p_all2

# using patchwork

design <- "AAAA
           BBBB
           CCCC
           DDDD
           EE##
           FFFF"


p_behavioural2 + p_infectious2 + p_noninfectious2 + p_chronic2 + p_deathAtBirth2 + p_other2 +
  plot_layout(design = design) + plot_annotation(title = "Risk Difference (RD) for different causes of death")


##############
# figures 
# lower female
##################

mod_table_lower_f <- rbind(res_other$mod_table[1, ],
                           res_deathAtBirth$mod_table[1, ],
                           res_chronic$mod_table[1, ],
                           res_noninfectious$mod_table[1, ],
                           res_infectious$mod_table[1, ],
                           res_behavioural$mod_table[1, ]
                           )

data_lower_f <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Female_Lower_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Female_Lower_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Female_Lower_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Female_Lower_Chronic", ],
                      res_deathAtBirth$data[res_deathAtBirth$data$moderator == "Female_Lower_Death", ],
                      res_other$data[res_other$data$moderator == "Female_Lower_Other", ])

mod_table_lower_f$name <- gsub("Female_Lower_", "", mod_table_lower_f$name)
mod_table_lower_f$name <- factor(mod_table_lower_f$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                              labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                         "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )
data_lower_f$moderator <- gsub("Female_Lower_", "", data_lower_f$moderator)
data_lower_f$moderator <- factor(data_lower_f$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                              labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                         "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )

res_lower_f <- list(mod_table = mod_table_lower_f, data = data_lower_f)

class(res_lower_f) <-  c("orchard", "data.frame")

p_lower_f <- orchard_plot(res_lower_f, mod = "sex_type",
            xlab = "Risk difference (Lower)", group = "Species", angle = 0) + ylim(-0.85, 0.7) + labs(title = "Female") + 
  theme(axis.title.x = element_blank(), axis.text.x  = element_blank(), axis.ticks.x = element_blank())
  #theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank())

# median female

mod_table_median_f <- rbind(res_other$mod_table[2, ],
                           res_deathAtBirth$mod_table[2, ],
                           res_chronic$mod_table[2, ],
                           res_noninfectious$mod_table[2, ],
                           res_infectious$mod_table[2, ],
                           res_behavioural$mod_table[2, ]
                           )

data_median_f <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Female_Median_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Female_Median_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Female_Median_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Female_Median_Chronic", ],
                      res_deathAtBirth$data[res_deathAtBirth$data$moderator == "Female_Median_Death", ],
                      res_other$data[res_other$data$moderator == "Female_Median_Other", ]
                      )

mod_table_median_f$name <- gsub("Female_Median_", "", mod_table_median_f$name)
mod_table_median_f$name <- factor(mod_table_median_f$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                              labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                         "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )
data_median_f$moderator <- gsub("Female_Median_", "", data_median_f$moderator)
data_median_f$moderator <- factor(data_median_f$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                              labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                         "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )

res_median_f <- list(mod_table = mod_table_median_f, data = data_median_f)

class(res_median_f) <-  c("orchard", "data.frame")

p_median_f <- orchard_plot(res_median_f, mod = "sex_type",
            xlab = "Risk difference (Median)", group = "Species", angle = 0) + ylim(-0.85, 0.7) + 
  theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank()) + 
  theme(axis.title.x = element_blank(), axis.text.x  = element_blank(), axis.ticks.x = element_blank())

# upper female

mod_table_upper_f <- rbind(res_other$mod_table[3, ],
                           res_deathAtBirth$mod_table[3, ],
                           res_chronic$mod_table[3, ],
                           res_noninfectious$mod_table[3, ],
                           res_infectious$mod_table[3, ],
                           res_behavioural$mod_table[3, ]
                           )

data_upper_f <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Female_Upper_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Female_Upper_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Female_Upper_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Female_Upper_Chronic", ],
                      res_deathAtBirth$data[res_deathAtBirth$data$moderator == "Female_Upper_Death", ],
                      res_other$data[res_other$data$moderator == "Female_Upper_Other", ]
                      ) 

mod_table_upper_f$name <- gsub("Female_Upper_", "", mod_table_upper_f$name)
mod_table_upper_f$name <- factor(mod_table_upper_f$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                                  labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                 "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )
data_upper_f$moderator <- gsub("Female_Upper_", "", data_upper_f$moderator)
data_upper_f$moderator <- factor(data_upper_f$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Death", "Other")), 
                                  labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                 "Chronic\ndisease", "Death\nat birth", "Other\ncauses")) )

res_upper_f <- list(mod_table = mod_table_upper_f, data = data_upper_f)

class(res_upper_f) <-  c("orchard", "data.frame")

p_upper_f <- orchard_plot(res_upper_f, mod = "sex_type",
                           xlab = "Risk difference (Upper)", group = "Species", angle = 0) + ylim(-0.85, 0.7) + 
  theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank()) + 
  theme(axis.title.x = element_blank(), axis.text.x  = element_blank(), axis.ticks.x = element_blank())

# lower male

mod_table_lower_m <- rbind(res_other$mod_table[4, ],
                           res_chronic$mod_table[4, ],
                           res_noninfectious$mod_table[4, ],
                           res_infectious$mod_table[4, ],
                           res_behavioural$mod_table[4, ]
                           )

mod_table_lower_m$name <- as.character(mod_table_lower_m$name)

data_lower_m <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Male_Lower_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Male_Lower_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Male_Lower_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Male_Lower_Chronic", ],
                      res_other$data[res_other$data$moderator == "Male_Lower_Other", ])

data_lower_m$moderator <- as.character(data_lower_m$moderator)

mod_table_lower_m$name <- gsub("Male_Lower_", "", mod_table_lower_m$name)
mod_table_lower_m$name <- factor(mod_table_lower_m$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic","Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )
data_lower_m$moderator <- gsub("Male_Lower_", "", data_lower_m$moderator)
data_lower_m$moderator <- factor(data_lower_m$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )

res_lower_m <- list(mod_table = mod_table_lower_m, data = data_lower_m)

class(res_lower_m) <-  c("orchard", "data.frame") 

p_lower_m <- orchard_plot(res_lower_m, mod = "sex_type",
                          xlab = "Risk difference (Lower)", group = "Species", angle = 0) + ylim(-0.85, 0.7) + labs(title = "Male") #+ 
 #theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank())


# median male

mod_table_median_m <- rbind(res_other$mod_table[5, ],
                           res_chronic$mod_table[5, ],
                           res_noninfectious$mod_table[5, ],
                           res_infectious$mod_table[5, ],
                           res_behavioural$mod_table[5, ]
                           )

mod_table_median_m$name <- as.character(mod_table_median_m$name)

data_median_m <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Male_Median_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Male_Median_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Male_Median_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Male_Median_Chronic", ],
                      res_other$data[res_other$data$moderator == "Male_Median_Other", ])

data_median_m$moderator <- as.character(data_median_m$moderator)

mod_table_median_m$name <- gsub("Male_Median_", "", mod_table_median_m$name)
mod_table_median_m$name <- factor(mod_table_median_m$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic","Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )
data_median_m$moderator <- gsub("Male_Median_", "", data_median_m$moderator)
data_median_m$moderator <- factor(data_median_m$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )

res_median_m <- list(mod_table = mod_table_median_m, data = data_median_m)

class(res_median_m) <-  c("orchard", "data.frame") 

p_median_m <- orchard_plot(res_median_m, mod = "sex_type",
                          xlab = "Risk difference (Median)", group = "Species", angle = 0) + ylim(-0.85, 0.7)  +
  theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank())
                                          
# upper male

mod_table_upper_m <- rbind(res_other$mod_table[6, ],
                           res_chronic$mod_table[6, ],
                           res_noninfectious$mod_table[6, ],
                           res_infectious$mod_table[6, ],
                           res_behavioural$mod_table[6, ]
                           )

mod_table_upper_m$name <- as.character(mod_table_upper_m$name)

data_upper_m <- rbind(res_behavioural$data[res_behavioural$data$moderator == "Male_Upper_Behavioural", ],
                      res_infectious$data[res_infectious$data$moderator == "Male_Upper_Infectious", ],
                      res_noninfectious$data[res_noninfectious$data$moderator == "Male_Upper_None-infectious", ],
                      res_chronic$data[res_chronic$data$moderator == "Male_Upper_Chronic", ],
                      res_other$data[res_other$data$moderator == "Male_Upper_Other", ])

data_upper_m$moderator <- as.character(data_upper_m$moderator)

mod_table_upper_m$name <- gsub("Male_Upper_", "", mod_table_upper_m$name)
mod_table_upper_m$name <- factor(mod_table_upper_m$name, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic","Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )
data_upper_m$moderator <- gsub("Male_Upper_", "", data_upper_m$moderator)
data_upper_m$moderator <- factor(data_upper_m$moderator, levels = rev(c("Behavioural", "Infectious", "None-infectious", "Chronic", "Other")), 
                                 labels = rev(c("Behavioural", "Infectious\ndisease", "None-infectious\ndisease", 
                                                "Chronic\ndisease", "Other\ncauses")) )

res_upper_m <- list(mod_table = mod_table_upper_m, data = data_upper_m)

class(res_upper_m) <-  c("orchard", "data.frame")
 
p_upper_m <- orchard_plot(res_upper_m, mod = "sex_type",
                          xlab = "Risk difference (Upper)", group = "Species", angle = 0) + ylim(-0.85, 0.7) +  
  theme(axis.title.y = element_blank(), axis.text.y  = element_blank(), axis.ticks.y = element_blank())


# all the data for causes of death saving as rds each (6 of them)

saveRDS(res_lower_f, here("Rdata", "fig", "res_lower_f.rds"))
saveRDS(res_median_f, here("Rdata", "fig", "res_median_f.rds"))
saveRDS(res_upper_f, here("Rdata", "fig", "res_upper_f.rds"))
saveRDS(res_lower_m, here("Rdata", "fig", "res_lower_m.rds"))
saveRDS(res_median_m, here("Rdata", "fig", "res_median_m.rds"))
saveRDS(res_upper_m, here("Rdata", "fig", "res_upper_m.rds"))

######################
# drowning
#########################

p_female <- p_lower_f + p_median_f + p_upper_f +
   plot_annotation(title = "Female")

p_male <- p_lower_m + p_median_m + p_upper_m +
  plot_annotation(title = "Male")

p_female /p_male + plot_layout(heights = c(6,5))

27 PART V: EXTRA FIGURE CODE

# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# Project: Contraception paper
# Version: 01
# Author:
# Description: Main Figures
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>


# INIT ----

rm(list = ls())

# libraries

if (!"devtools" %in% installed.packages())
  install.packages("devtools")
library(devtools)

if (!"tidyverse" %in% installed.packages())
  install.packages("tidyverse")
library(tidyverse)

# Plotting phylogenetic tree
if (!"ggtree" %in% installed.packages())
  install.packages("ggtree")
library(ggtree)

# Aligning plots
if (!"patchwork" %in% installed.packages())
  install.packages("patchwork")
library(patchwork)

# Aligning phylogenetic plots
if (!"aplot" %in% installed.packages())
  install.packages("aplot")
library(aplot)

# Manipulating trees
if (!"ape" %in% installed.packages())
  install.packages("ape")
library(ape)

# Dependency of OrchaRd package
if (!"metafor" %in% installed.packages())
  install.packages("metafor")
library(metafor)

#  Orchard plots
if (!"orchaRd" %in% installed.packages())
  devtools::install_github("daniel1noble/orchaRd", ref = "main", force = TRUE)
library(orchaRd)

# Importing data

library(here)

# Color palettes ----
colpal_sex <- c("Female" = "#de2d26", "Male" = "#4292c6")

colpal_gonads <- c(
  "Gonads not removed \n(female)" = "#de2d26",
  "Gonads removed \n(female)" = "#ee9490",
  "Gonads removed \n(male)" = "#78b1d6"
)

colpal_sextype <- c(
  "Male_surgical" = "#4292c6",
  "Male_hormonal" = "#78b1d6",
  "Female_surgical" = "#ee9490",
  "Female_hormonal" = "#de2d26"
)

colpal_order <- c(
  "Artiodactyla" = "#E69F00",
  "Primates" = "#0072B2",
  "Carnivora" = "#D55E00",
  "Perissodactyla" = "#56B4E9",
  "Diprotodontia" = "#009E73",
  "Rodentia" = "#9966FF",
  "Lagomorpha" = "#CC79A7",
  "Didelphimorphia" = "#F0E442",
  "Chiroptera" = "#999999",
  "Hyracoidea" = "#661100"
)

colpal_cod <- c(
  "Trauma" = "#AA4499",
  "Infectious\ndisease" = "#332288",
  "None-infectious\ndisease" = "#117733",
  "Chronic\ndisease" = "#DDCC77",
  "Death\nat birth" = "#CC6677",
  "Other\ncauses" = "#88CCEE"
)


colpal_contra <- c("Contracepted" = "#D55E00",
                   "Not contracepted" = "#009E73")

colpal_type <- c(
  "Surgical" = "#1b7837",
  "Hormonal" = "#762a83",
  "Immunological" = "#E69F00"
)


colpal_moderator <- c(
  "Controlled" = "#762a83",
  "Not controlled" = "#af8dc3",
  "Sham-controlled" = "#1b7837",
  "No sham" = "#7fbf7b",
  "Wild or \nSemi-wild" = "#01665e",
  "Others \n(e.g., lab, farm)" = "#5ab4ac"
)


# Ggplot themes ----

# Orchard plot theme
theme_orchard <- function(family = "Helvetica") {
  `%+replace%`(
    theme_bw(base_size = 12, base_family = family),
    theme(
      # general plot style
      aspect.ratio = 0.7,
      plot.background = element_blank(),
      plot.margin = margin(
        t = 5.5,
        r = 20,
        b = 5.5,
        l = 0
      ),
      # panel style
      panel.background = element_blank(),
      panel.border = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.3
      ),
      panel.spacing = unit(0.05, "cm"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(linetype = "solid", linewidth = 0.3),
      # strip style
      strip.background = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.3
      ),
      strip.text = element_text(size = 9, margin = margin(0.1, 0, 0.1, 0, "cm")),
      # axis settings
      axis.title = element_text(size = 9),
      axis.text = element_text(size = 9),
      axis.line = element_blank(),
      axis.ticks = element_line(linewidth = 0.3),
      axis.ticks.length = unit(.05, "cm"),
      # legend settings
      legend.title = element_text(size = 9),
      legend.text = element_text(size = 8),
      legend.background = element_rect(fill = "transparent", colour = NA),
      legend.position = "bottom",
      legend.key = element_rect(fill = "transparent", colour = NA)
    )
  )
}

# Bar plot theme
theme_bar <- function(family = "Helvetica") {
  `%+replace%`(
    theme_bw(base_size = 9, base_family = family),
    theme(
      plot.background = element_blank(),
      plot.margin = margin(
        t = 0,
        r = 0,
        b = 0,
        l = 0
      ),
      panel.background = element_blank(),
      panel.border = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.3
      ),
      panel.spacing = unit(0.05, "cm"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(linetype = "solid", linewidth = 0.3),
      strip.background = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.3
      ),
      strip.text = element_text(margin = margin(0.1, 0, 0.1, 0, "cm"), size = 8),
      axis.title.x = element_text(vjust = -2),
      axis.text.x = element_text(
        size = 6,
        vjust = -1.5,
        color = "black"
      ),
      axis.ticks.x = element_line(linewidth = 0.3),
      axis.title.y = element_blank(),
      axis.text.y = element_blank(),
      axis.ticks.y = element_blank(),
      axis.ticks.length = unit(.05, "cm"),
      legend.position = "none"
    )
  )
}

theme_segment <- function(family = "Helvetica") {
  `%+replace%`(
    theme_bw(base_size = 9, base_family = family),
    theme(
      plot.background = element_blank(),
      plot.margin = margin(
        t = 0,
        r = 0,
        b = 0,
        l = 0
      ),
      panel.background = element_blank(),
      panel.border = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.3
      ),
      panel.spacing = unit(0.05, "cm"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(linetype = "solid", linewidth = 0.3),
      axis.title.x = element_text(vjust = -2),
      axis.text.x = element_text(
        size = 9,
        vjust = -1.5,
        color = "black"
      ),
      axis.text.y = element_text(
        size = 9,
        colour = "black",
        hjust = 0.95
      ),
      axis.ticks = element_line(linewidth = 0.3),
      axis.ticks.length = unit(.05, "cm"),
      axis.title.y = element_blank(),
      legend.position = "none"
    )
  )
}

# Phylogentic tree theme
theme_phylotree <- function(family = "Helvetica") {
  `%+replace%`(
    theme_bw(base_size = 9, base_family = family),
    theme(
      # plot layout
      plot.margin = margin(
        t = 0,
        r = 0,
        b = 0,
        l = 0
      ),
      plot.background = element_blank(),
      # panel layout
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(linetype = "solid", linewidth = 0.2),
      panel.background = element_blank(),
      panel.border = element_rect(
        color = "grey70",
        fill = NA,
        linewidth = 0.2
      ),
      panel.spacing = unit(0.1, "cm"),
      # panel.grid.major.y = element_blank(),
      # strip layout
      # strip.background = element_blank(),
      strip.background = element_rect(
        color = "grey70",
        fill = NA,
        linewidth = 0.2
      ),
      strip.text = element_text(margin = margin(0.1, 0, 0.1, 0, "cm")),
      # axis layout
      axis.title = element_text(size = 9),
      axis.text.y = element_blank(),
      axis.line.y = element_blank(),
      axis.line.x = element_line(color = "black", linewidth = 0.3),
      axis.ticks.y = element_blank(),
      axis.text.x = element_text(size = 7),
      axis.ticks.x = element_line(linewidth = 0.3),
      axis.ticks.length.x = unit(.05, "cm"),
      # legend layout
      legend.background = element_rect(fill = "transparent", colour = NA),
      legend.key = element_rect(fill = "transparent", colour = NA),
      legend.title = element_text(size = 7),
      legend.text = element_text(size = 6),
      legend.key.size = unit(0.5, "cm")
    )
  )
}

# Kaplan meier curves theme
theme_km <- function(family = "Helvetica") {
  `%+replace%`(
    theme_bw(base_size = 12, base_family = family),
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(linetype = "solid", linewidth = 0.3),
      plot.title = element_text(face = "bold", hjust = 0),
      # aspect.ratio = 1,
      axis.title.x = element_text(size = 9, vjust = -0.5),
      axis.title.y = element_blank(),
      axis.text = element_text(size = 6),
      axis.line = element_line(linewidth = 0.3),
      axis.ticks = element_line(linewidth = 0.3),
      axis.ticks.length = unit(.05, "cm"),
      legend.title = element_text(size = 9),
      legend.text = element_text(size = 8),
      strip.text = element_text(size = 6, face = "italic"),
      # remove the plot background and border
      plot.background = element_blank(),
      panel.background = element_blank(),
      panel.border = element_blank(),
      panel.spacing = unit(0.05, "cm"),
      # make the legend and strip background transparent
      legend.background = element_rect(fill = "transparent", colour = NA),
      legend.position = "bottom",
      legend.key = element_rect(fill = "transparent", colour = NA),
      strip.background = element_rect(fill = "transparent", colour = NA)
    )
  )
}

# Functions ----

# Plotting function for Kaplan Meier Plots
PlotLifeTab <- function(ltdat, title, ncol = 4) {
  ord <- ltdat %>%
    group_by(species, order) %>%
    tally() %>%
    mutate(Ages = 1, ple = 1, type = NA)
  fig <- ltdat %>%
    ggplot(aes(x = Ages, y = ple, color = type)) +
    geom_rect(
      dat = ord,
      aes(fill = order),
      xmin = -Inf,
      xmax = Inf,
      ymin = -Inf,
      ymax = Inf,
      alpha = 0.2
    ) +
    geom_step(size = 0.4) +
    geom_vline(
      aes(xintercept = minAge),
      lty = 3,
      color = "black",
      size = 0.3
    ) +
    xlab("Age") +
    ylab("Survival") +
    ggtitle(title) +
    facet_wrap(
      . ~ species,
      scales = "free_x",
      strip.position = c("top"),
      ncol = ncol,
      labeller = label_wrap_gen(multi_line = T, width = 18)
    ) +
    scale_color_manual(values = colpal_contra,
                       name = "",
                       na.translate = F) +
    scale_fill_manual(values = colpal_order, name = "") +
    scale_y_continuous(breaks = c(0, 0.5, 1)) +
    expand_limits(x = 0) +
    theme_km() +
    ggh4x::force_panelsizes(rows = unit(1.6, "cm"), cols = unit(1.6, "cm"))
  return(fig)
}


# DATA ----

# Phylogenetic maximum credibility tree
load(here("data2", "maxCredTree.RData"))
tree <- maxCred
rm(maxCred)

# Taxonomy table
tax <- read.csv(here("data2", "FinalTranslTab.csv"))

# Zoo data
dat0 <- read.csv(here("data2", "resultsBaSTApost2005oldFormatFeb2025.csv"))
dat <- dat0 %>% left_join(tax, by = c("species" = "ZIMSspecies"))
dat <- dat %>%
  mutate(phylogeny = gsub(" ", "_", vertlifeSpecies))

# Fixing species name
dat$species[dat$species == "Aonyx cinereus"] <- "Aonyx cinerea"

# Basta summary results post 05
resTab_post05 <- read.csv(file = here("data2", "resultsBaSTApost05.csv"))

# Basta summary results pre 05
resTab_pre05 <- read.csv(file = sprintf(here("data2", "resultsBaSTApre05.csv")))

# Basta summary results before and after maturity
resTab_mat <- read.csv(here("data2", "resultsBaSTAbefAftMatur.csv"))

# Data for Kaplan Meier plots males surgical post 05
dat_males <- read.csv(here("data2", "km_males_surg_post05.csv"))

# Data for Kaplen Meier plots females hormonal pre 05
dat_fem_pre05 <- read.csv(here("data2", "km_females_horm_pre05.csv"))

# Data for Kaplen Meier plots females hormonal pre 05
dat_fem_horm_post05 <- read.csv(here("data2", "km_females_horm_post05.csv"))

# Data for Kaplen Meier plots females surgical post 05
dat_fem_surg_post05 <- read.csv(here("data2", "km_females_surg_post05.csv"))


# excel file with vet responses
vetr <- readxl::read_excel(here("data2", "Species classification as castration or vasectomy_03 AF.xlsx"))

# Load model outputs from zoo_rev2.qmp
load("results/model_outputs/zoo/mod_all3.RData") # Fig 1b)

# Load data generated by literature.qmd
results <- readRDS("results/model_outputs/literature/results.rds") # Fig 3a)
ddat <- readRDS("data2/ddat.rds") # Fig 3a)
load(here("results", "model_outputs/literature/mod_sex1.RData")) # Fig 3b)
load(here("results", "model_outputs/literature/mod_rem.RData")) # Fig 3c)
load(here("results", "model_outputs/literature/mod_con1.RData")) # Fig 4a)
load(here("results", "model_outputs/literature/mod_sham.RData")) # Fig 4b)
load(here("results", "model_outputs/literature/mod_env1.RData")) # Fig 4c)
load(here("results", "model_outputs/literature/mod_sex_mat.RData")) # Fig 5a)
mod_healthspan_sex <- readRDS("results/model_outputs/literature/mod_healthspan_sex.rds")

# Load data on causes of death generated by all.qmd Fig 1c)
res_lower_f <- readRDS(here("results", "model_outputs/zoo/res_lower_f.rds"))
res_upper_f <- readRDS(here("results", "model_outputs/zoo/res_upper_f.rds"))
res_lower_m <- readRDS(here("results", "model_outputs/zoo/res_lower_m.rds"))
res_upper_m <- readRDS(here("results", "model_outputs/zoo/res_upper_m.rds"))

# Fig 3b)
res <- readRDS(here("data2", "res.rds"))
kdat <- readRDS(here("data2", "kdat.rds"))
ldat <- readRDS(here("data2", "ldat.rds"))

# FIGURES ----

# Fig 1 ----
# Fig 1a) Phylo tree lifespans ----
# Phylo tree zoos with lifespans

# recalculated these rations with the new data - Please check if this is correct!
pdat2 <- dat %>% mutate(
  lrr_surgical_m = (Male_Surgical_Contra_Mean / Male_Surgical_NoContra_Mean - 1) * 100,
  lrr_surgical_f = (
    Female_Surgical_Contra_Mean / Female_Surgical_NoContra_Mean - 1
  ) * 100,
  lrr_hormonal_m = (Male_Hormonal_Contra_Mean / Male_Hormonal_NoContra_Mean - 1) * 100,
  lrr_hormonal_f = (
    Female_Hormonal_Contra_Mean / Female_Hormonal_NoContra_Mean - 1
  ) * 100
)

# reformat data to long format
pdat3 <- pdat2 %>% select(
  species,
  vertlifeSpecies,
  order,
  phylogeny,
  lrr_surgical_m,
  lrr_surgical_f,
  lrr_hormonal_m,
  lrr_hormonal_f
)

pdat_long <- pdat3 %>% pivot_longer(cols = c(
  lrr_surgical_m,
  lrr_surgical_f,
  lrr_hormonal_m,
  lrr_hormonal_f
))

pdat_long %>% mutate(sex = rep(c("male", "female"), nrow(pdat_long) / 2), type = rep(
  c("surgical", "surgical", "hormonal", "hormonal"),
  nrow(pdat_long) / 4
)) -> pdat_long

pdat_long %>% mutate(
  min_value = ifelse(is.na(value) == TRUE, NA, ifelse(value < 0, value, 0)),
  max_value = ifelse(is.na(value) == TRUE, NA, ifelse(value > 0, value, 0))
) -> pdat_long

pdat_long$type <- factor(
  pdat_long$type,
  levels = c("surgical", "hormonal"),
  labels = c("Surgical", "Hormonal")
)

# Subset tree
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% pdat_long$phylogeny))]

to_drop # Why not in tree?

tree_subset <- drop.tip(tree, to_drop)

table(tree_subset$tip.label %in% pdat_long$phylogeny)
table(pdat_long$phylogeny %in% tree_subset$tip.label)

pdat_long <- pdat_long[which(pdat_long$phylogeny %in% tree_subset$tip.label), ]

#  use getMRCA() to obtain common ancestor nodes to position the order silhouttes
tree.tibble <- tidytree::as_tibble(tree_subset)
ord <- unique(pdat_long$order)

dford <- data.frame(order = ord, node = NA)

for (i in ord) {
  tip <- unique(as.data.frame(pdat_long[which(pdat_long$order == i), "phylogeny"])$phylogeny)
  if (length(tip) > 1) {
    tnode <- getMRCA(tree_subset, tip = tip)
  } else if (length(tip) == 1) {
    tnode <-
      match(tip, tree.tibble$label)
  }
  
  dford[dford$order == i, "node"] <- tnode
}

# merge dford with color palette
dford <- colpal_order %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  rename(order = rowname, color = ".") %>%
  right_join(dford, by = c("order"))

# for orders with less than 2 species we remove the color
dford[dford$order %in% c("Hyracoidea", "Didelphimorphia", "Lagomorpha"), "color"] <- NA

p <- ggtree(
  tree_subset,
  layout = "rectangular",
  ladderize = T,
  size = 0.2,
  color = "grey50"
) +
  xlim(0, 180) +
  geom_hilight(node = dford$node,
               fill = dford$color,
               alpha = .2)
p



#  barplot showing male surgical and female hormonal
p_bar <- pdat_long %>%
  filter(!(type == "Surgical" &
             sex == "female"),
         !(type == "Hormonal" &
             sex == "male")) %>%
  mutate(
    sex = case_match(sex, "female" ~ "Female", "male" ~ "Male"),
    sex = factor(sex, levels = c("Male", "Female")),
    type = case_match(type, "Surgical" ~ "Male surgical", "Hormonal" ~ "Female hormonal"),
    type = factor(type, levels = c("Male surgical", "Female hormonal"))
  ) %>%
  ggplot(aes(x = value, y = phylogeny, fill = sex)) +
  geom_col() +
  scale_fill_manual(values = colpal_sex, name = "Sex") +
  geom_vline(
    aes(xintercept = 0),
    lty = 3,
    color = "black",
    linewidth = 0.3
  ) +
  facet_wrap( ~ type,
              ncol = 2,
              labeller = label_wrap_gen(multi_line = T, width = 15)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
  theme_phylotree() +
  xlab("Ratio: Contracepted / Normal (%)") +
  ylab("") +
  theme(legend.position = "none")
p_bar


# final fig
# we use aplot to properly match the bars to the phylo plots (does not work with patchwork)
p_tree_final <- p_bar %>% aplot::insert_left(p, width = c(0.8, 1))
p_tree_final

ggsave(
  here("results", "plots", "Fig1a_phylotreeZIMS.pdf"),
  width = 100,
  height = 125,
  units = "mm"
)

# Fig 1b) Orchard plots sex & contraception type ----
theme_orchard_updated <- theme_orchard() +
  theme(
    legend.position = "right",
    aspect.ratio = 0.7,
    axis.text = element_text(size = 11),
    axis.title = element_text(size = 11)
  )

p_combo <- orchard_plot(mod_all3,
                        group = "species",
                        xlab = "LnRR (Effect size)",
                        mod = "sex_type") +
  theme_orchard() +
  scale_colour_manual(values = colpal_sextype) +
  scale_fill_manual(values = colpal_sextype) +
  scale_x_discrete(labels = rev(
    c(
      "Male surgical",
      "Male hormonal",
      "Female surgical",
      "Female hormonal"
    )
  )) +
  theme_orchard_updated
p_combo
#ggsave("results/plots/Fig1b_orchardZIMS.pdf", width = 100, units = "mm")

# Fig 1c) Causes of death ----

p_lower_f <- orchard_plot(
  res_lower_f,
  mod = "sex_type",
  xlab = "Risk difference (Young adult)",
  group = "Species",
  angle = 0
) + ylim(-0.85, 0.7) +
  theme_orchard() +
  scale_x_discrete(labels = rev(
    c(
      "Behaviour",
      "Infectious disease",
      "Non-infectious disease",
      "Chronic disease",
      "Death at birth",
      "Other causes"
    )
  )) +
  scale_colour_manual(values = colpal_cod) +
  scale_fill_manual(values = colpal_cod) +
  theme_orchard_updated

p_upper_f <- orchard_plot(
  res_upper_f,
  mod = "sex_type",
  xlab = "Risk difference (Old adult)",
  group = "Species",
  angle = 0
) + ylim(-0.85, 0.7) +
  theme_orchard() +
  scale_x_discrete(labels = rev(
    c(
      "Behaviour",
      "Infectious disease",
      "Non-infectious disease",
      "Chronic disease",
      "Death at birth",
      "Other causes"
    )
  )) +
  scale_colour_manual(values = colpal_cod) +
  scale_fill_manual(values = colpal_cod) +
  theme_orchard_updated

p_lower_m <- orchard_plot(
  res_lower_m,
  mod = "sex_type",
  xlab = "Risk difference (Young adult)",
  group = "Species",
  angle = 0
) + ylim(-0.85, 0.7) +
  scale_x_discrete(labels = rev(
    c(
      "Behaviour",
      "Infectious disease",
      "Non-infectious disease",
      "Chronic disease",
      "Other causes"
    )
  )) +
  theme_orchard() +
  scale_colour_manual(values = colpal_cod) +
  scale_fill_manual(values = colpal_cod) +
  theme_orchard_updated

p_upper_m <- orchard_plot(
  res_upper_m,
  mod = "sex_type",
  xlab = "Risk difference (Old adult)",
  group = "Species",
  angle = 0
) + ylim(-0.85, 0.7) +
  scale_x_discrete(labels = rev(
    c(
      "Behaviour",
      "Infectious disease",
      "Non-infectious disease",
      "Chronic disease",
      "Other causes"
    )
  )) +
  theme_orchard() +
  scale_colour_manual(values = colpal_cod) +
  scale_fill_manual(values = colpal_cod) +
  theme_orchard_updated


p_combo +  p_upper_m + p_upper_f + plot_layout(ncol = 1)
ggsave(
  "results/plots/Fig1bc_SexType_CausesOfDeath.pdf",
  width = 170,
  height = 330,
  units = "mm"
)


# Fig 2 - Kaplan Meier survival  -----

# plotting order for taxonomic orders
order_levs <- c(
  "Artiodactyla",
  "Perissodactyla",
  "Carnivora",
  "Chiroptera",
  "Primates",
  "Rodentia",
  "Lagomorpha",
  "Diprotodontia",
  "Didelphimorphia"
)

# Fig 2a) Vasectomy ----
# Vasectomy post 2005
# subset species with vasectomy from vet response file
sp_vasec <- vetr %>%
  filter(`Castration or Vasectomy` == "Likely vasectomized") %>%
  pull(Species)
# remove lions as it will be in panel D
dat_vasec <- dat_males %>% filter(species %in% sp_vasec)

# change species order according to taxonomic orders
dat_vasec <- dat_vasec %>% arrange(factor(order, levels = order_levs), species)
dat_vasec$species <- factor(dat_vasec$species, levels = unique(dat_vasec$species))

# Plot
PlotLifeTab(ltdat = dat_vasec, title = "Males - Vasectomy")

ggsave(here("results", "plots", "Fig2a_Males_VasecPost05.pdf"))

# Fig 2b) Castration ----

# Castration post 2005
# subset species with castration from vet response file
sp_castr <- vetr %>%
  filter(`Castration or Vasectomy` == "Castration") %>%
  pull(Species)
# only species with > 80 individuals in the contraception group
#sp_castr <- sp_castr[sp_castr %in% resTab_post05[resTab_post05$Ncontra >= 80, "Species"]]

dat_castr <- dat_males %>% filter(species %in% sp_castr)

# order species names by order
dat_castr <- dat_castr %>% arrange(factor(order, levels = order_levs), species)
dat_castr$species <- factor(dat_castr$species, levels = unique(dat_castr$species))

# Plot
PlotLifeTab(ltdat = dat_castr,
            title = "Castration (males)",
            ncol = 5)

# Save plot
ggsave(here("results", "plots", "Fig2b_Males_Castrpost05.pdf"))


# Fig 2c) Hormonal ----

# Hormonal post 2005 females
# change species order according to taxonomic orders
dat_fem_horm_post05 <- dat_fem_horm_post05 %>% arrange(factor(order, levels = order_levs), species)
dat_fem_horm_post05$species <- factor(dat_fem_horm_post05$species,
                                      levels = unique(dat_fem_horm_post05$species))

# only species with > 80 individuals in the contraception group
sp_fhorm <- as.character(unique(dat_fem_horm_post05$species))
sp_fhorm <- sp_fhorm[sp_fhorm %in% resTab_post05[resTab_post05$Ncontra >= 70 &
                                                   resTab_post05$Sex == "Female" &
                                                   resTab_post05$Type == "Hormonal", "Species"]]
sort(sp_fhorm)
# exlude lions as they will be in panel D
sp_fhorm <- sp_fhorm[!sp_fhorm == "Panthera leo"]
dat_fem_horm_post05_sub <- dat_fem_horm_post05 %>% filter(species %in% sp_fhorm)

# Plot
PlotLifeTab(ltdat = dat_fem_horm_post05_sub,
            title = "Hormonal (females)",
            ncol = 5)
ggsave(here("results", "plots", "Fig2c_Females_Hormonalpost05.pdf"))



# Fig 2d) Female lions ----

# Females progesterone based contraception
flion_prog <- dat_fem_pre05 %>% filter(species == "Panthera leo")
flion_prog$method <- "Progesterone"
flion_gnrh <- dat_fem_horm_post05 %>% filter(species == "Panthera leo")
flion_gnrh$method <- "GnRH"
flion_surg <- dat_fem_surg_post05 %>% filter(species == "Panthera leo")
flion_surg$method <- "Surgical"

dat_flion <- rbind(flion_prog, flion_gnrh, flion_surg)
dat_flion$method <- factor(dat_flion$method, levels = c("Progesterone", "GnRH", "Surgical"))
levels(dat_flion$method)
p_lion <- ggplot(dat_flion, aes(x = Ages, y = ple, color = type)) +
  geom_rect(
    dat = ord,
    aes(fill = order),
    xmin = -Inf,
    xmax = Inf,
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2
  ) +
  geom_step(size = 0.4) +
  geom_vline(
    aes(xintercept = minAge),
    lty = 3,
    color = "black",
    size = 0.3
  ) +
  xlab("Age") +
  ylab("Survival") +
  ggtitle("Panthera leo") +
  facet_wrap(
    . ~ method,
    scales = "free_x",
    strip.position = c("top"),
    ncol = 3,
    labeller = label_wrap_gen(multi_line = T, width = 18)
  ) +
  scale_color_manual(values = colpal_contra,
                     name = "",
                     na.translate = F) +
  scale_fill_manual(values = colpal_order, name = "") +
  scale_y_continuous(breaks = c(0, 0.5, 1)) +
  expand_limits(x = 0) +
  theme_km() +
  ggh4x::force_panelsizes(rows = unit(1.6, "cm"), cols = unit(1.6, "cm"))
p_lion
ggsave(here("results", "plots", "Fig2d_female_lions.pdf"))



# Fig 3 Meta analysis -----
# Fig 3a) Lifespan bar plot ----

sum_data <- data.frame(
  "x.diamond" = c(
    results$lowerCL,
    results$estimate,
    results$upperCL,
    results$estimate
  ),
  "y.diamond" = c(1, 1 + 0.25, 1, 1 - 0.25)
)

ddat$Species_Latin <- factor(
  ddat$Species_Latin,
  levels = c(
    "Overall",
    "Lamperta fluviatilis",
    # fish
    "Oncorhynchus nerka",
    # fish
    "Oncorhynchus masou",
    # fish
    "Anolis sagrei",
    # lizard
    "Phascolarctos cinereus",
    # masp
    "Trichosurus vulpecula",
    # masp?
    "Oryctolagus cuniculus",
    # rabbit
    "Mesocricetus auratus",
    "Myodes glareolus",
    # voles
    "Rattus norvegicus",
    # rat
    "Rattus argentiventer",
    # rat
    "Mus musculus",
    "Ovis aries",
    "Equus ferus",
    "Odocoileus virginianus",
    "Vulpes vulpes",
    "Canis lupus",
    "Felis catus",
    "Varecia variegata",
    "Varecia rubra",
    "Macaca fascicularis",
    "Homo sapiens"
  ),
  labels = c(
    "Overall",
    "Lamperta fluviatilis",
    # fish
    "Oncorhynchus nerka",
    # fish
    "Oncorhynchus masou",
    # fish
    "Anolis sagrei",
    # lizard
    "Phascolarctos cinereus",
    # masp
    "Trichosurus vulpecula",
    # masp?
    "Oryctolagus cuniculus",
    # rabbit
    "Mesocricetus auratus",
    "Myodes glareolus",
    # voles
    "Rattus norvegicus",
    # rat
    "Rattus argentiventer",
    # rat
    "Mus musculus",
    "Ovis aries",
    "Equus ferus",
    "Odocoileus virginianus",
    "Vulpes vulpes",
    "Canis lupus",
    "Felis catus",
    "Varecia variegata",
    "Varecia rubra",
    "Macaca fascicularis",
    "Homo sapiens"
  )
)


phy_sex <- ggplot(data = ddat, aes(x = yi, y = Species_Latin)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "black") +
  geom_vline(
    xintercept = results$estimate,
    linetype = 1,
    colour = "red"
  ) +
  geom_errorbarh(
    aes(
      xmin = lower.ci,
      xmax = upper.ci,
      colour = Sex
    ),
    height = 0,
    show.legend = TRUE,
    linewidth = 3,
    position = position_dodge(width = 0.75)
  ) +
  geom_point(
    aes(col = Sex),
    fill = "white",
    size = 1.6,
    shape = 21,
    position = position_dodge2(width = 0.75)
  ) +
  coord_cartesian(xlim = c(-1, 1.5)) +
  #xlim(-1.3, 1.6) +
  # creating 95% prediction intervals
  geom_segment(data = results,
               ggplot2::aes(
                 x = lowerPR,
                 y = 1,
                 xend = upperPR,
                 yend = 1,
                 group = name
               )) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data,
                        ggplot2::aes(x = x.diamond, y = y.diamond),
                        fill = "red") +
  scale_color_manual(values = colpal_sex) +
  labs(x = "LnRR (Effect size)", y = "", colour = "Sex") +
  theme_segment() +
  theme(axis.text.y = element_text(face = "italic"))

phy_sex
# adding icons
filenames <- list.files(here("icons2", "literature"),
                        pattern = ".png",
                        full.names = TRUE)
ldf <- lapply(filenames, png::readPNG)
names(ldf) <- substr(filenames, 18, 18 + 60)

xpos <- c(-1.2, -0.5)

phy_sex <- phy_sex +
  annotation_custom(
    grid::rasterGrob(ldf$Lampetra_fluviatilis.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 1.5,
    ymax = 2.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Oncorhynchus_nerka.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 2.5,
    ymax = 3.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Oncorhynchus_masou.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 3.5,
    ymax = 4.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Anolis_sagrei.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 4.5,
    ymax = 5.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Phascolarctos_cinereus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 5.5,
    ymax = 6.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Trichosurus_vulpecula.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 6.5,
    ymax = 7.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Oryctolagus_cuniculus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 7.5,
    ymax = 8.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Mesocricetus_auratus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 8.5,
    ymax = 9.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Myodes_glareolus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 9.5,
    ymax = 10.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_norvegicus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 10.5,
    ymax = 11.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_argentiventer.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 11.5,
    ymax = 12.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Mus_musculus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 12.5,
    ymax = 13.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Ovis_aries.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 13.5,
    ymax = 14.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Equus_ferus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 14.5,
    ymax = 15.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Odocoileus_virginianus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 15.5,
    ymax = 16.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Vulpes_vulpes.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 16.5,
    ymax = 17.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Canis_lupus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 17.5,
    ymax = 18.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Felis_catus.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 18.5,
    ymax = 19.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Varecia_variegata.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 19.5,
    ymax = 20.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Varecia_rubra.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 20.5,
    ymax = 21.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Macaca_Fascicularis.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 21.5,
    ymax = 22.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Homo_sapiens.png),
    xmin = xpos[1],
    xmax = xpos[2],
    ymin = 22.5,
    ymax = 23.5
  )

phy_sex


# Fig 3b) Healthspan ----
sum_data <- data.frame(
  "x.diamond" = c(res$lowerCL, res$estimate , res$upperCL, res$estimate),
  "y.diamond" = c(1, 1 + 0.25, 1, 1 - 0.25)
)

# plotting Sub.measure
# looking at
#dat$Sub.measure
kdat$Sub.measure <- factor(kdat$Sub.measure,
                           levels = rev(
                             c(
                               "Cardiac\nfunction/\npathology",
                               "Cardiac size",
                               "Cognition",
                               "Frailty",
                               "Immune\nfunction",
                               "Metabolism",
                               "Muscle size",
                               "Non-tumor\npathology",
                               "Sensory\nfunction",
                               "Strength/\nbalance",
                               "Tumor\nmammory",
                               "Tumor\nnonmammory",
                               "Voluntary\nactivity",
                               "Overall"
                             )
                           ),
                           labels = rev(
                             c(
                               "Cardiac function/\npathology",
                               "Cardiac size",
                               "Cognition",
                               "Frailty",
                               "Immune function",
                               "Metabolism",
                               "Muscle size",
                               "Non-tumor\npathology",
                               "Sensory function",
                               "Strength/balance",
                               "Tumor mammory",
                               "Tumor\nnon-mammory",
                               "Voluntary activity",
                               "Overall"
                             )
                           ))
## version 1 ----

mes_sex1 <- ggplot(data = kdat, aes(x = yi, y = Sub.measure)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "black") +
  geom_vline(
    xintercept = res$estimate,
    linetype = 1,
    colour = "red"
  ) +
  geom_errorbarh(
    aes(
      xmin = lower.ci,
      xmax = upper.ci,
      colour = Sex
    ),
    height = 0,
    show.legend = TRUE,
    linewidth = 3,
    position = position_dodge(width = 0.75)
  ) +
  geom_point(
    aes(col = Sex),
    fill = "white",
    size = 1.6,
    shape = 21,
    position = position_dodge2(width = 0.75)
  ) +
  xlim(-1.6, 1.6) +
  #creating 95% prediction intervals
  geom_segment(data = res,
               ggplot2::aes(
                 x = lowerPR,
                 y = 1,
                 xend = upperPR,
                 yend = 1,
                 group = name
               )) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data,
                        ggplot2::aes(x = x.diamond, y = y.diamond),
                        fill = "red") +
  scale_color_manual(values = colpal_sex) +
  labs(x = "LnRR (Effect size)", y = "", colour = "Sex") +
  theme_segment() +
  theme(legend.position = "bottom") 
mes_sex1

mes_sex1 <- mes_sex1 + 
  annotation_custom(
  grid::rasterGrob(ldf$Mus_musculus.png),
  xmin = -1.5,
  xmax = -0.7,
  ymin = 12.5,
  ymax = 15
) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_argentiventer.png),
    xmin = -1,
    xmax = -0.3,
    ymin = 12.5,
    ymax = 14
  )

## version 2 ----
# with cutoff x-axis
mes_sex2 <- ggplot(data = kdat, aes(x = yi, y = Sub.measure)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "black") +
  geom_vline(
    xintercept = res$estimate,
    linetype = 1,
    colour = "red"
  ) +
  geom_errorbarh(
    aes(
      xmin = lower.ci,
      xmax = upper.ci,
      colour = Sex
    ),
    height = 0,
    show.legend = TRUE,
    linewidth = 3,
    position = position_dodge(width = 0.75)
  ) +
  geom_point(
    aes(col = Sex),
    fill = "white",
    size = 1.6,
    shape = 21,
    position = position_dodge2(width = 0.75)
  ) +
  coord_cartesian(xlim = c(-0.5, 0.85)) +
  #creating 95% prediction intervals
  geom_segment(data = res,
               ggplot2::aes(
                 x = lowerPR,
                 y = 1,
                 xend = upperPR,
                 yend = 1,
                 group = name
               )) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data,
                        ggplot2::aes(x = x.diamond, y = y.diamond),
                        fill = "red") +
  scale_color_manual(values = colpal_sex) +
  labs(x = "LnRR (Effect size)", y = "", colour = "Sex") +
  theme_segment() +
  theme(legend.position = "bottom") +
  # add left arrrow
  geom_segment(
    data = subset(kdat, upper.ci < -0.5),
    aes(
      x = -0.4,
      y = Sub.measure,
      xend = -0.55,
      yend = Sub.measure
    ),
    arrow = arrow(length = unit(0.2, "cm"), type = "closed"),
    color = "grey30",
    size = 1.2
  )
mes_sex2

mes_sex2 <- mes_sex2 + 
  annotation_custom(
    grid::rasterGrob(ldf$Mus_musculus.png),
    xmin = -0.5,
    xmax = -0.2,
    ymin = 12.5,
    ymax = 15
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_argentiventer.png),
    xmin = -0.4,
    xmax = -0.1,
    ymin = 12,
    ymax = 14
  )


## version 3 ----
# remove immune function
mes_sex3 <- kdat %>% filter(!Sub.measure == "Immune function") %>%
  ggplot(aes(x = yi, y = Sub.measure)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "black") +
  geom_vline(
    xintercept = res$estimate,
    linetype = 1,
    colour = "red"
  ) +
  geom_errorbarh(
    aes(
      xmin = lower.ci,
      xmax = upper.ci,
      colour = Sex
    ),
    height = 0,
    show.legend = TRUE,
    linewidth = 3,
    position = position_dodge(width = 0.75)
  ) +
  geom_point(
    aes(col = Sex),
    fill = "white",
    size = 1.6,
    shape = 21,
    position = position_dodge2(width = 0.75)
  ) +
  coord_cartesian(xlim = c(-0.5, 0.85)) +
  #creating 95% prediction intervals
  geom_segment(data = res,
               ggplot2::aes(
                 x = lowerPR,
                 y = 1,
                 xend = upperPR,
                 yend = 1,
                 group = name
               )) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data,
                        ggplot2::aes(x = x.diamond, y = y.diamond),
                        fill = "red") +
  scale_color_manual(values = colpal_sex) +
  labs(x = "LnRR (Effect size)", y = "", colour = "Sex") +
  theme_segment() +
  theme(legend.position = "bottom")


mes_sex3 <- mes_sex3 + 
  annotation_custom(
    grid::rasterGrob(ldf$Mus_musculus.png),
    xmin = -0.5,
    xmax = -0.2,
    ymin = 11,
    ymax = 15
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_argentiventer.png),
    xmin = -0.4,
    xmax = -0.1,
    ymin = 11,
    ymax = 13
  )
mes_sex3

# Align and save Figure 3
phy_sex + mes_sex + plot_layout(guides = 'keep')
ggsave(
  "results/plots/Fig3ab_life_healthspan.pdf",
  width = 170,
  units = "mm",
  height = 160
)

phy_sex + mes_sex2 + plot_layout(guides = 'keep')
ggsave(
  "results/plots/Fig3ab_life_healthspan_v2.pdf",
  width = 165,
  units = "mm",
  height = 160
)

phy_sex + mes_sex3 + plot_layout(guides = 'keep')
ggsave(
  "results/plots/Fig3ab_life_healthspan_v3.pdf",
  width = 165,
  units = "mm",
  height = 160
)




# Fig 4 - Moderator effects ----
# Fig 4a) Sex ----
mod_sex1$data$Sex <- factor(mod_sex1$data$Sex, levels = rev(c("Male", "Female")))

p_sex <- orchard_plot(
  mod_sex1,
  mod = "Sex",
  xlab = "LnRR (Effect size)",
  group = "Study",
  cb = F,
  angle = 0
) +
  theme_orchard() +
  scale_fill_manual(values = colpal_sex) +
  scale_color_manual(values = colpal_sex) +
  theme(legend.position = "none")

p_sex


# Fig 4b) Gonad removal ----
mod_rem$data$Sex_Gonads <- factor(mod_rem$data$Sex_Gonads, levels = rev(
  c(
    "Gonads removed \n(male)",
    "Gonads not removed \n(female)",
    "Gonads removed \n(female)"
  )
))

p_gonad <- orchard_plot(
  mod_rem,
  mod = "Sex_Gonads",
  xlab = "LnRR (Effect size)",
  group = "Study",
  cb = F,
  angle = 0
) +
  theme_orchard() +
  theme(legend.position = "none") +
  scale_fill_manual(values = colpal_gonads) +
  scale_color_manual(values = colpal_gonads)

p_gonad

# Fig 4c)  Control ----
# Effects of different moderators

p_mod <- orchard_plot(mod_con1,
                      mod = "Controlled_treatments",
                      xlab = "LnRR (Effect size)",
                      group = "Study") +
  scale_fill_manual(values = colpal_moderator) +
  scale_colour_manual(values = colpal_moderator) +
  theme_orchard() +
  theme(legend.position = "none")

p_mod

# Fig 4d) Sham ----

p_sham <- orchard_plot(mod_sham,
                       mod = "Shamtreatment_moderator",
                       xlab = "Log response ratio (lnRR)",
                       group = "Study") +
  scale_fill_manual(values = colpal_moderator) +
  scale_colour_manual(values = colpal_moderator) +
  theme_orchard() +
  theme(legend.position = "none")

p_sham

# Fig 4e) Environment -----

p_env <- orchard_plot(mod_env1,
                      mod = "Wild_or_semi_wild",
                      xlab = "LnRR (Effect size)",
                      group = "Study") +
  scale_fill_manual(values = colpal_moderator) +
  scale_colour_manual(values = colpal_moderator) +
  theme_orchard() +
  scale_x_discrete(labels = c("Others \n(e.g., lab, farm)", "Wild or \nsemi-wild"))
p_env

# Fig 4f) Healthspan ----
# orchard_healthspan
p_health <- orchard_plot(mod_healthspan_sex,
                         mod = "Sex",
                         xlab = "LnRR (Effect size)",
                         group = "Study") +
  scale_fill_manual(values = colpal_sex) +
  scale_colour_manual(values = colpal_sex) +
  theme_orchard()
p_health

# Align and save Figure 4

p_sex + p_gonad +  p_mod +  p_sham + p_env  + p_health + plot_layout(guides = "keep", ncol = 1)

p_mod + p_sham + p_env + plot_layout(guides = "keep",
                                     ncol = 1,
                                     axes = "collect")




ggsave(
 here("results", "plots", "Fig4_Moderator_plot.pdf"),
  width = 200,
  height = 170,
  units = "mm"
)

# Fig 5 - Timing of contraception ----
# Fig 5a) ----

# Males should come before females in the plot, but for some reason it does not change if i change the factor levels...

mod_sex_mat$data$Sex <- factor(mod_sex_mat$data$Sex, levels = rev(c("Male", "Female")))
levels(mod_sex_mat$data$Sex)

p_mat <- bubble_plot(
  mod_sex_mat,
  xlab = "Life-course stages",
  ylab = "LnRR (Effect size)",
  mod = "Maturity_at_treatment_ordinal",
  group = "Study",
  by = "Sex",
  condition.nrow = 2
) +
  theme_orchard() +
  scale_fill_manual(values = colpal_sex) +
  theme(aspect.ratio = 0.7)
p_mat
ggsave(
  here("results", "plots", "Fig5a_bubbleplot.pdf"),
  width = 100,
  height = 100,
  units = "mm"
)


# Fig 5b) ----
# Phylo tree before and after maturity

tree <- maxCred
# rm(maxCred)

# join tax to data
resTab_mat <- resTab_mat %>% left_join(tax, by = c("Species" = "ZIMSspecies"))

# Calculate life expectancy difference (before: contraception before age at mat / after: contraception after age at mat)
resTab_mat$bef <- log(resTab_mat$LifeExpContraBef / resTab_mat$LifeExpNoContra)
resTab_mat$aft <- log(resTab_mat$LifeExpContraAft / resTab_mat$LifeExpNoContra)

resTab_mat$vertlifeSpecies <- gsub(" ", "_", resTab_mat$vertlifeSpecies)

# Subset tree
to_drop <-
  tree$tip.label[which(!(tree$tip.label %in% resTab_mat$vertlifeSpecies))]
tree_subset <- drop.tip(tree, to_drop)

#  Use getMRCA() to obtain common ancestor nodes to position the order silhouttes
tree.tibble <- tidytree::as_tibble(tree_subset)
ord <- unique(resTab_mat$order)

dford <- data.frame(order = ord, node = NA)

for (i in ord) {
  tip <- resTab_mat[which(resTab_mat$order == i), "vertlifeSpecies"]
  if (length(tip) > 1) {
    tnode <- getMRCA(tree_subset, tip = tip)
  } else if (length(tip) == 1) {
    tnode <-
      match(tip, tree.tibble$label)
  }
  
  dford[dford$order == i, "node"] <- tnode
}

# merge dford with color palette
dford <- colpal_order %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  rename(order = rowname, color = ".") %>%
  right_join(dford, by = c("order"))

resTab_mat <- resTab_mat %>%
  select(Species, vertlifeSpecies, order, bef, aft) %>%
  pivot_longer(cols = c("bef", "aft"))

# replace tree names with the ZIMS names
sp <- unique(resTab_mat[order(match(resTab_mat$vertlifeSpecies, tree_subset$tip.label)), "Species"])
tree_subset$tip.label <- sp$Species

# plot
p_tree <- ggtree(
  tree_subset,
  layout = "rectangular",
  ladderize = T,
  size = 0.2,
  color = "grey50"
) +
  xlim(0, 300) +
  geom_tiplab(fontface = 3, size = 2) +
  geom_hilight(node = dford$node,
               fill = dford$color,
               alpha = .2)
p_tree

# Plot of life expectancy differences
p_bar <- resTab_mat %>%
  mutate(
    name = case_match(name, "bef" ~ "Before maturity", "aft" ~ "After maturity"),
    name = factor(name, levels = c("Before maturity", "After maturity")),
  ) %>%
  ggplot(aes(x = value, y = Species)) +
  geom_col(width = 0.6, fill = "grey40") +
  xlab("Log ratio: Contraceptive / Normal") +
  ylab("") +
  facet_wrap(. ~ name) +
  theme_bar()
p_bar

# Combine phylo tree and bar plot using aplot
p_tree_mat <- p_bar %>% aplot::insert_left(p_tree, width = c(1, 1))
p_tree_mat

ggsave(
  here("results", "plots", "Fig5b_Phyloplot_mat.pdf"),
  width = 100,
  height = 120,
  units = "mm"
)

# Fig S1) - Supplemental Kaplan Meier ----

# Fig S2 - Supplemental Measurement type Healthspan ----


#gdat$S
ldat$Measurement.type <- factor(ldat$Measurement.type,
                                levels = rev(
                                  c(
                                    "Adrenal-Cortical adenoma",
                                    "Autoshaping learning test",
                                    "balance on a dowel",
                                    "Barnes maze test",
                                    "cardiac fibrosis",
                                    "Cardiac pathology",
                                    "Cardiomyocyte size",
                                    "E/A ratio",
                                    "Ejection fraction",
                                    "Energy expenditure",
                                    "fractional shortening",
                                    "Frailty score",
                                    "Glucose tolerance",
                                    "Grip strength",
                                    "Harderian adenoma",
                                    "Heart size",
                                    "inhibitory avoidance test",
                                    "Insulin sensitivity",
                                    "Kidney pathology",
                                    "left ventricle size",
                                    "Left Ventricular Isovolumic Relaxation Time",
                                    "Liver pathology",
                                    "Mammary pathology",
                                    "Morris water maze",
                                    "nonthymic lymphosarcoma",
                                    "novel object recognition",
                                    "Open field test",
                                    "pituitary tumors",
                                    "Polyarteritis",
                                    "Proportion with hypophyseal adenoma",
                                    "Proportion with tumors ",
                                    "Pulmonary adenoma",
                                    "Quadriceps size",
                                    "RAM memory test",
                                    "Reticulum cell sarcoma",
                                    "rotarod",
                                    "Skeletal muscle fiber size",
                                    "Smell preference",
                                    "superficial skin and subcutaneous tumor not mammary",
                                    "T cell function test",
                                    "T maze test",
                                    "thymic lymphomas",
                                    "Total activity dark period",
                                    "total non-neoplastic lesions",
                                    "Treamill",
                                    "vision presence of cataracts",
                                    "voluntary activity assessment",
                                    "voluntary wheel running",
                                    "water radial arm maze test",
                                    "Y maze testing",
                                    "Overall"
                                  )
                                ),
                                labels = rev(
                                  c(
                                    "Adrenal-cortical adenoma",
                                    "Autoshaping learning test",
                                    "Balance on a dowel",
                                    "Barnes maze test",
                                    "Cardiac fibrosis",
                                    "Cardiac pathology",
                                    "Cardiomyocyte size",
                                    "E/A ratio",
                                    "Ejection fraction",
                                    "Energy expenditure",
                                    "Fractional shortening",
                                    "Frailty score",
                                    "Glucose tolerance",
                                    "Grip strength",
                                    "Harderian adenoma",
                                    "Heart size",
                                    "Inhibitory avoidance test",
                                    "Insulin sensitivity",
                                    "Kidney pathology",
                                    "Left ventricle size",
                                    "Left ventricular IRT",
                                    "Liver pathology",
                                    "Mammary pathology",
                                    "Morris water maze",
                                    "Nonthymic lymphosarcoma",
                                    "Novel object recognition",
                                    "Open field test",
                                    "Pituitary tumors",
                                    "Polyarteritis",
                                    "Prop. with hypophyseal adenoma",
                                    "Proportion with tumors",
                                    "Pulmonary adenoma",
                                    "Quadriceps size",
                                    "RAM memory test",
                                    "Reticulum cell sarcoma",
                                    "Rotarod",
                                    "Skeletal muscle fiber size",
                                    "Smell preference",
                                    "Superfic. skin & subcut. tumor n.m.",
                                    "T cell function test",
                                    "T maze test",
                                    "Thymic lymphomas",
                                    "Total activity dark period",
                                    "Total non-neoplastic lesions",
                                    "Treadmill",
                                    "Vision presence of cataracts",
                                    "Voluntary activity assessment",
                                    "Voluntary wheel running",
                                    "Water radial arm maze test",
                                    "Y maze testing",
                                    "Overall"
                                  )
                                ))

mestype_sex <- ggplot(data = ldat, aes(x = yi, y = Measurement.type)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "black") +
  geom_vline(
    xintercept = res$estimate,
    linetype = 1,
    colour = "red"
  ) +
  geom_errorbarh(
    aes(
      xmin = lower.ci,
      xmax = upper.ci,
      colour = Sex
    ),
    height = 0,
    show.legend = TRUE,
    linewidth = 2,
    position = position_dodge(width = 0.75)
  ) +
  geom_point(
    aes(col = Sex),
    fill = "white",
    size = 1.1,
    shape = 21,
    position = position_dodge2(width = 0.75)
  ) +
  xlim(-3.5, 2) +
  #creating 95% prediction intervals
  geom_segment(data = res,
               ggplot2::aes(
                 x = lowerPR,
                 y = 1,
                 xend = upperPR,
                 yend = 1,
                 group = name
               )) +
  # creating diamonsts (95% CI)
  ggplot2::geom_polygon(data = sum_data,
                        ggplot2::aes(x = x.diamond, y = y.diamond),
                        fill = "red") +
  theme_bw() +
  scale_color_manual(values = colpal_sex) +
  labs(x = "LnRR (Effect size)", y = "", colour = "Sex") +
  theme_segment() +
  theme(plot.margin = margin(
    t = 2,
    r = 6,
    b = 6,
    l = 1
  ))

mestype_sex

# icons

filenames <- list.files(here("icons2", "literature"),
                        pattern = ".png",
                        full.names = TRUE)
ldf <- lapply(filenames, png::readPNG)
names(ldf) <- substr(filenames, 18, 18 + 60)

mestype_sex <- mestype_sex +
  annotation_custom(
    grid::rasterGrob(ldf$Mus_musculus.png),
    xmin = -1.5 * 2.5,
    xmax = -1 * 2.5,
    ymin = 13.5 * 3.5,
    ymax = 14.5 * 3.5
  ) +
  annotation_custom(
    grid::rasterGrob(ldf$Rattus_argentiventer.png),
    xmin = -1 * 2.5,
    xmax = -0.5 * 2.5,
    ymin = 13 * 3.5,
    ymax = 14 * 3.5
  )

mestype_sex
ggsave(
  here("results", "plots", "FigS2_life_healthspan.pdf"),
  width = 90,
  units = "mm",
  height = 165
)

28 R Session Informtion

sessionInfo() %>% pander()

R version 4.4.2 (2024-10-31)

Platform: aarch64-apple-darwin20

locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8

attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: phytools(v.2.4-4), maps(v.3.4.2.1), ggtree(v.3.14.0), ggstance(v.0.3.7), ggimage(v.0.3.3), apextra(v.0.0.0.1), cowplot(v.1.1.3), ggalluvial(v.0.12.5), GoodmanKruskal(v.0.0.3), naniar(v.1.1.0), formatR(v.1.14), here(v.1.0.1), png(v.0.1-8), MuMIn(v.1.48.11), clubSandwich(v.0.6.0), orchaRd(v.2.0), rotl(v.3.1.0), emmeans(v.1.11.0), readxl(v.1.4.5), lme4(v.1.1-37), patchwork(v.1.3.0), kableExtra(v.1.4.0), ape(v.5.8-1), pander(v.0.6.6), metafor(v.4.8-0), numDeriv(v.2016.8-1.1), metadat(v.1.4-0), Matrix(v.1.7-3), lubridate(v.1.9.4), forcats(v.1.0.0), stringr(v.1.5.1), dplyr(v.1.1.4), purrr(v.1.0.4), readr(v.2.1.5), tidyr(v.1.3.1), tibble(v.3.2.1), ggplot2(v.3.5.2) and tidyverse(v.2.0.0)

loaded via a namespace (and not attached): mathjaxr(v.1.6-0), RColorBrewer(v.1.1-3), rstudioapi(v.0.17.1), jsonlite(v.2.0.0), magrittr(v.2.0.3), ggbeeswarm(v.0.7.2), TH.data(v.1.1-3), estimability(v.1.5.1), magick(v.2.8.6), corrplot(v.0.95), farver(v.2.1.2), nloptr(v.2.2.1), rmarkdown(v.2.29), fs(v.1.6.6), vctrs(v.0.6.5), minqa(v.1.2.8), htmltools(v.0.5.8.1), progress(v.1.2.3), DEoptim(v.2.2-8), cellranger(v.1.1.0), gridGraphics(v.0.5-1), htmlwidgets(v.1.6.4), sandwich(v.3.1-1), zoo(v.1.8-14), igraph(v.2.1.4), iterators(v.1.0.14), lifecycle(v.1.0.4), pkgconfig(v.2.0.3), R6(v.2.6.1), fastmap(v.1.2.0), rbibutils(v.2.3), digest(v.0.6.37), aplot(v.0.2.5), rprojroot(v.2.0.4), labeling(v.0.4.3), latex2exp(v.0.9.6), clusterGeneration(v.1.3.8), timechange(v.0.3.0), mgcv(v.1.9-3), httr(v.1.4.7), compiler(v.4.4.2), bit64(v.4.6.0-1), withr(v.3.0.2), doParallel(v.1.0.17), optimParallel(v.1.0-2), MASS(v.7.3-65), scatterplot3d(v.0.3-44), tools(v.4.4.2), vipor(v.0.4.7), rncl(v.0.8.7), beeswarm(v.0.4.0), rentrez(v.1.2.3), visdat(v.0.6.0), quadprog(v.1.5-8), glue(v.1.8.0), nlme(v.3.1-168), generics(v.0.1.3), gtable(v.0.3.6), tzdb(v.0.5.0), hms(v.1.1.3), xml2(v.1.3.8), foreach(v.1.5.2), pillar(v.1.10.2), vroom(v.1.6.5), yulab.utils(v.0.2.0), splines(v.4.4.2), treeio(v.1.30.0), lattice(v.0.22-7), bit(v.4.6.0), survival(v.3.8-3), tidyselect(v.1.2.1), knitr(v.1.50), reformulas(v.0.4.0), svglite(v.2.1.3), stats4(v.4.4.2), xfun(v.0.52), expm(v.1.0-0), stringi(v.1.8.7), lazyeval(v.0.2.2), ggfun(v.0.1.8), yaml(v.2.3.10), pacman(v.0.5.1), boot(v.1.3-31), evaluate(v.1.0.3), codetools(v.0.2-20), ggplotify(v.0.1.2), cli(v.3.6.5), xtable(v.1.8-4), systemfonts(v.1.2.2), Rdpack(v.2.6.4), Rcpp(v.1.0.14), coda(v.0.19-4.1), XML(v.3.99-0.18), parallel(v.4.4.2), prettyunits(v.1.2.0), phangorn(v.2.12.1), viridisLite(v.0.4.2), mvtnorm(v.1.3-3), tidytree(v.0.4.6), scales(v.1.4.0), crayon(v.1.5.3), combinat(v.0.0-8), rlang(v.1.1.6), fastmatch(v.1.1-6), mnormt(v.2.1.1) and multcomp(v.1.4-28)